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Analytics and Types
After watching this video; you will be able to recognize key characteristics and types of analytics.
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Data Mining
After watching this video; you will be able to distinguish between the two aspects of data mining.
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Data Types and Data Preprocessing
After watching this video; you will be able to differentiate between data acquisition and data reduction.
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Finance and Healthcare Applications
After watching this video; you will be able to illustrate a finance and healthcare application of predictive analytics.
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Fraud Detection and Risk Management
After watching this video; you will be able to illustrate a fraud detection or risk management application of predictive analytics.
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Generalized Linear Models
After watching this video; you will be able to define generalized linear model and provide an example of its use.
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Logistic Regression
After watching this video; you will be able to define logistic regression model and provide an example of its use.
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Machine Learning
After watching this video; you will be able to define machine learning models and provide an example of their use.
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Management Decision Support Systems
After watching this video; you will be able to illustrate a management decision support system application of predictive analytics.
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Modeling Tools – Open Source and Commercial
After watching this video; you will be able to name key analytics tools on the market or open-source tools.
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Neural Networks
After watching this video; you will be able to define neural network models and provide an example of their use.
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Models
After watching this video; you will be able to identify common predictive analytics models.
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Overview
After watching this video; you will be able to identify key characteristics and examples of predictive analytics.
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Propensity Modeling
After watching this video; you will be able to define propensity models and provide an example of its use.
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ROI Analysis
After watching this video; you will be able to use predictive analytics to support an ROI decision.
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Sales and Marketing Applications
After watching this video; you will be able to illustrate a sales and marketing application of predictive analytics.
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Statistical Models
After watching this video; you will be able to identify statistical models and provide examples of their use.
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Technology and Big Data Influences
After watching this video; you will be able to recognize technology and big data influences on predictive analytics.
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Times Series Analysis
After watching this video; you will be able to analyze time series data using a common analysis technique.
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Uplift Models
After watching this video; you will be able to define uplift models and provide an example of their use.
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Using a Predictive Modeling Tool
After watching this video; you will be able to perform a simple application of predictive analytics using sample data and a software tool.
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Clustering
After watching this video; you will be able to identify applications of clustering.
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Decision Trees
After watching this video; you will be able to define decision tree model and provide an example of its use.
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Predictive Analytics: Overview of Text Mining
After watching this video, you will be able to identify key characteristic of text mining.
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Predictive Analytics: K-Means Clustering Termination Procedures
After watching this video, you will be able to recognize key steps for the termination of K-Means Clustering iterations.
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Predictive Analytics: Evaluation and Considerations for K-Means Clustering
After watching this video, you will be able to evaluate K-Means Clustering.
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Predictive Analytics: Overview of K-Means Clustering
After watching this video, you will be able to list key features of K-Means Clustering.
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Predictive Analytics: Minimizing SSE of Data Points
After watching this video, you will be able to recognize key steps for reducing the sum of squared errors in K-Means Clustering.
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Predictive Analytics: DBSCAN Attributes
After watching this video, you will be able to identify key attributes for performing DBSCAN.
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Predictive Analytics: Hierarchical Clustering and DBSCAN Overview
After watching this video, you will be able to list key features of Hierarchical Clustering and DBSCAN.
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Predictive Analytics: DBSCAN Operation
After watching this video, you will be able to recognize key steps in DBSCAN.
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Predictive Analytics: Ordinary Least Squares (OLS)
After watching this video, you will be able to determine the OLS parameters.
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Predictive Analytics: Drawing Inferences
After watching this video, you will be able to make regression inferences.
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Predictive Analytics: Linear Regression Overview
After watching this video, you will be able to recognize characteristics of linear regression.
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Predictive Analytics: Sum of Squared Errors
After watching this video, you will be able to calculate sum of squared errors.
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Predictive Analytics: Limitations of Naïve Bayes
After watching this video, you will be able to identify various limitations of Naïve Bayes.
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Predictive Analytics: Bayesian Belief Networks
After watching this video, you will be able to recognize features of Bayesian Belief Networks.
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Predictive Analytics: Naïve Bayes Overview
After watching this video, you will be able to recognize key features of Naïve Bayes.
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Predictive Analytics: Predicting Outcomes with Naïve Bayes
After watching this video, you will be able to calculate the probability of an event occurring with Naïve Bayes.
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Predictive Analytics: Determining the Optimal Hyperplane
After watching this video, you will be able to determine the optimal hyperplane.
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Predictive Analytics: Exercise: Use A/B Testing
After watching this video, you will be able to predict outcomes using A/B testing.
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Predictive Analytics: Support Vector Machines Overview
After watching this video, you will be able to identify features of Support Vector Machines.
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Predictive Analytics: Data Transformation Techniques
After watching this video, you will be able to recognize how to transform linear non-separable data to linear separable data.
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Predictive Analytics: Types of Clustering Techniques
After watching this video, you will be able to identify the different types of clustering.
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Predictive Analytics: Proximity Measures for Clustering
After watching this video, you will be able to calculate proximity.
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Predictive Analytics: Introduction to Clustering
After watching this video, you will be able to recognize characteristics of clustering.
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Predictive Analytics: Overview of A/B Testing
After watching this video, you will be able to recognize what A/B testing is and where it is applicable.
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Predictive Analytics: Autoregressive Models
After watching this video, you will be able to identify features of autoregressive models.
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Predictive Analytics: Moving Average Models
After watching this video, you will be able to identify features of moving average models.
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Predictive Analytics: Stationary and Nonstationary Data Series
After watching this video, you will be able to distinguish between stationary time series and nonstationary time series data.
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Predictive Analytics: Time Series Decomposition
After watching this video, you will be able to recognize the various components of time series data.
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Predictive Analytics: Apply Time Series Modeling Concepts
After watching this video, you will be able to apply time series modeling concepts.
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Predictive Analytics: Machine Learning Overview
After watching this video, you will be able to identify key features of machine learning.
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Predictive Analytics: Autoregressive Moving Average (ARMA) Models
After watching this video, you will be able to identify features of ARMA models.
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Predictive Analytics: Parameterization and Forecasting
After watching this video, you will be able to identify various steps required to make a forecast.
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Predictive Analytics: A/B Testing Features
After watching this video, you will be able to establish an A/B test hypothesis and determine what to test.
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Predictive Analytics: Implementing A/B Testing
After watching this video, you will be able to implement A/B testing for web site optimization.
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Predictive Analytics: Social Network Mapping
After watching this video, you will be able to identify key features of social network mapping.
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Predictive Analytics: Key Terms and Concepts
After watching this video, you will be able to recognize key terms and concepts used in social network and media analysis.
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Predictive Analytics: Assigning Across Document Predictor Variables
After watching this video, you will be able to assign across document predictor variables for text mining.
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Predictive Analytics: Term Frequency and Inverse Document Frequency
After watching this video, you will be able to recognize the use of term frequency and inverse document frequency measures for text mining.
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Predictive Analytics: Assigning within Document Predictor Variables
After watching this video, you will be able to assign within document predictor variables for text mining.
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Predictive Analytics: Text Normalization
After watching this video, you will be able to recognize key methods for text normalization.
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Predictive Analytics: Overview of Social Network Analysis
After watching this video, you will be able to identify key characteristics of social network analysis.
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Predictive Analytics: Ego-centric and Network-centric Analysis
After watching this video, you will be able to distinguish between ego-centric and network-centric analysis.
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Predictive Analytics: Sentiment Analysis
After watching this video, you will be able to identify key characteristics of sentiment analysis.
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Predictive Analytics: Text Mining Applications
After watching this video, you will be able to identify examples of text mining applications.
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Predictive Analytics: Time Series Overview
After watching this video, you will be able to identify key characteristics of time series forecasting.
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Predictive Analytics: Considerations for Model Performance
After watching this video, you will be able to identify key considerations of model validation.
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Predictive Analytics: Data Cleaning and Preparation
After watching this video, you will be able to recognize important aspects of data preparation for model development.
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Predictive Analytics: Data Pre-processing and Model Building
After watching this video, you will be able to identify key functions in the model creation process.
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Predictive Analytics: Descriptive Model Evaluation
After watching this video, you will be able to recognize key features of Occam's Razor.
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Predictive Analytics: Mean Squared Error Measures for Prediction
After watching this video, you will be able to calculate mean squared error measures.
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Predictive Analytics: Model Complexity and Resampling
After watching this video, you will be able to balance model complexity with overfitting.
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Predictive Analytics: Model Validation
After watching this video, you will be able to identify features of two-fold validation.
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Predictive Analytics: Lift and Gain Charts
After watching this video, you will be able to interpret lift and gain charts.
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Predictive Analytics: ROC and AUC
After watching this video, you will be able to interpret ROC curves and AUC.
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Predictive Analytics: Variation Measures for Prediction
After watching this video, you will be able to recognize prediction variation measures.
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Predictive Analytics: Evaluating Classification Models
After watching this video, you will be able to recognize evaluation measures for classification models.
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Predictive Analytics: Advanced Predictive Tools
After watching this video, you will be able to implement a random forest and an uplift model using an example dataset.
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Predictive Analytics: Understanding Business Objectives and Data
After watching this video, you will be able to identify key aspects for understanding the business problem and securing the right data.
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Predictive Analytics: Targeting with Uplift Models
After watching this video, you will be able to recognize who to target with uplift models.
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Predictive Analytics: How Uplift Models Work
After watching this video, you will be able to recognize how uplift models work.
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Predictive Analytics: Model Deployment Planning
After watching this video, you will be able to recognize the high-level activities and planning for model deployment.
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Predictive Analytics: Stakeholder Management
After watching this video, you will be able to identify important aspects of stakeholder management.
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Predictive Analytics: Model Development and Deployment
After watching this video, you will be able to recognize best practices for creating and deploying the forecasting model.
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Predictive Analytics: Business Validation and Benchmarks
After watching this video, you will be able to recognize the importance of business validation and the various types of benchmarks to assess the forecasting model project.
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Predictive Analytics: Model Building Process and Data Discovery
After watching this video, you will be able to recognize key phases in developing a model.
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Predictive Analytics: User Training and Model Documentation
After watching this video, you will be able to recognize the best practices for training users and developing project documentation.
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Predictive Analytics: Model Recalibration and Maintenance
After watching this video, you will be able to recognize the importance of monitoring and recalibrating the forecasting model.
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Predictive Analytics: Estimating Treatment Effects
After watching this video, you will be able to estimate treatment effects.
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Predictive Analytics: Example of Propensity Score Matching
After watching this video, you will be able to apply propensity score matching.
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Predictive Analytics: Propensity Score
After watching this video, you will be able to recognize key features of the propensity score.
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Predictive Analytics: Propensity Score Matching
After watching this video, you will be able to identify key features of propensity score matching.
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Predictive Analytics: Overview of Segmentation Modeling
After watching this video, you will be able to identify key features of segmentation modeling.
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Predictive Analytics: EDA and Cluster Segmentation Modeling
After watching this video, you will be able to distinguish between exploratory data analysis and cluster segmentations.
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Predictive Analytics: Random Forest Overview
After watching this video, you will be able to identify key features of random forests.
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Predictive Analytics: Random Forest Model Concepts
After watching this video, you will be able to identify key random forest model concepts.
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Predictive Analytics: Overview of Uplift Models
After watching this video, you will be able to identify key features of uplift models.
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Predictive Analytics: Decision Tree Characteristics
After watching this video, you will be able to identify key features of decision trees.
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Predictive Analytics: Random Forest Model Error Measurement
After watching this video, you will be able to recognize random forest performance measurements.
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Predictive Analytics: Interpreting Results and Testing Significance
After watching this video, you will be able to interpret logistic regression results.
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Predictive Analytics: Odds Ratio and Relative Risk
After watching this video, you will be able to calculate the odds ratio.
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Predictive Analytics: Logistic Regression Overview
After watching this video, you will be able to list key features of logistic regression.
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Predictive Analytics: Logit Transformation and the Likelihood Function
After watching this video, you will be able to recognize the logit transformation and likelihood functions.
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Predictive Analytics: Considerations for Logistic Regression
After watching this video, you will be able to recognize key considerations for logistic regression.
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Predictive Analytics: Linear Regression Statistical Inference
After watching this video, you will be able to determine and interpret the statistical significance of individual variables and of the overall model.
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Predictive Analytics: Machine Learning Tools and Process
After watching this video, you will be able to identify key tools used for machine learning and the high-level process steps.
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Predictive Analytics: Ensemble Techniques for Machine Learning
After watching this video, you will be able to identify key features of ensemble techniques.
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Predictive Analytics: Ensemble Performance Considerations and Metrics
After watching this video, you will be able to measure ensemble error rate.
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Predictive Analytics: Deep Learning
After watching this video, you will be able to identify key features of deep learning.
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Predictive Analytics: Supervised vs. Unsupervised Methods
After watching this video, you will be able to distinguish between supervised and nonsupervised learning methods.
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Predictive Analytics: Interpret Scatter Plots
After watching this video, you will be able to recognize the direction, form, and strength of a scatter plot.
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Predictive Analytics: Wrapper Data Reduction Method
After watching this video, you will be able to recognize key features of the wrapper data reduction method.
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Predictive Analytics: Factor Analysis
After watching this video, you will be able to recognize the key features of factor analysis.
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Predictive Analytics: Information Theory Approach to Feature Selection
After watching this video, you will be able to use the information theory approach for feature selection.
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Predictive Analytics: Chi-square Feature Selection Method
After watching this video, you will be able to recognize the key features of using Chi-square.
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Predictive Analytics: Run Charts and Scatter Plots
After watching this video, you will be able to use run charts and scatter plots to perform EDA.
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Predictive Analytics: Histograms and Stem-and-leaf Plots
After watching this video, you will be able to use histograms and stem-and-leaf plots to perform EDA.
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Predictive Analytics: Overview of EDA and Quantitative Techniques
After watching this video, you will be able to recognize key features of EDA and how quantitative techniques are used to perform EDA.
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Predictive Analytics: Bar Charts and Box-and-whisker Plots
After watching this video, you will be able to use bar charts and box-and-whisker plots to perform EDA.
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Predictive Analytics: Imputation for Continuous Data
After watching this video, you will be able to use imputation to replace missing data.
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Predictive Analytics: Dimension Reduction
After watching this video, you will be able to recognize key data reduction methodologies.
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Predictive Analytics: The Need to Clean Messy Data
After watching this video, you will be able to recognize what is tidy and what is untidy data.
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Predictive Analytics: Outlier Identification and Handling
After watching this video, you will be able to identify outliers and determine whether to remove these values.
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Predictive Analytics: Dummy Variables and Variable Removal
After watching this video, you will be able to recognize important aspects of setting dummy variables and removing variables.
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Predictive Analytics: Approaches for Handling Missing Data
After watching this video, you will be able to recognize key approaches for handling missing data.
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Predictive Analytics: Transforming, Normalizing, and Scaling Data
After watching this video, you will be able to perform data transformation, normalization, and scaling.
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Predictive Analytics: Variable Partitioning
After watching this video, you will be able to recognize important aspects of variable partitioning.
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Predictive Analytics: Principal Component Analysis for Numerical Data
After watching this video, you will be able to use principal component analysis for feature selection.
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Predictive Analytics: Distributions and the Probability Density Function
After watching this video, you will be able to identify features of a standard normal distribution.
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Predictive Analytics: Binomial and Poisson Distributions
After watching this video, you will be able to list features of the Binomial and Poisson distributions.
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Predictive Analytics: Data Mining Concepts and Techniques
After watching this video, you will be able to identify important data mining concepts and techniques.
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Predictive Analytics: Methods for Data Mining
After watching this video, you will be able to identify data mining methods used for predictive analysis.
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Predictive Analytics: One and Two-tailed Hypothesis Tests
After watching this video, you will be able to recognize key features of one and two-tailed hypothesis tests.
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Predictive Analytics: Choose an Appropriate Data Mining Method
After watching this video, you will be able to match analytics problems to appropriate data mining methods.
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Predictive Analytics: Introduction to Hypothesis Testing
After watching this video, you will be able to recognize key features of hypothesis testing and its application.
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Predictive Analytics: Exercise: Evaluate Classification Models
After watching this video, you will be able to determine the superior classification model.
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Predictive Analytics: Other Advanced Predictive Analytics Models
After watching this video, you will be able to identify key features of classification trees, neural networks, support vector machines, and Bayesian networks.
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Predictive Analytics: What is Predictive Analytics?
After watching this video, you will be able to recognize what predictive analytics is, the types of models used, and what its goals are.
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Predictive Analytics: Shedding Light with Predictive Analytics
After watching this video, you will be able to recognize what types of questions are answered by predictive analytics and who uses it.
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Predictive Analytics: Big Data Considerations and Sources
After watching this video, you will be able to recognize the considerations of using big data and what the sources are.
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Predictive Analytics: Time Series, Uplift, and Logistic Models
After watching this video, you will be able to identify key features of time series, uplift, and logistic models.
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Predictive Analytics: Features of Predictive Analytics Models
After watching this video, you will be able to identify key features of predictive analytics.
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Predictive Analytics: Big Data
After watching this video, you will be able to recognize key features of big data.
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Predictive Analytics: Predictive Analytics vs. Traditional BI
After watching this video, you will be able to recognize key differences between predictive analytics and traditional business intelligence.
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Predictive Analytics: Sales, Marketing, and Operations
After watching this video, you will be able to illustrate a marketing or sales application of predictive analytics.
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Predictive Analytics: Data Warehousing and Data Marts
After watching this video, you will be able to recognize the important features of data warehousing and marts.
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Predictive Analytics: Relational Database Management System and Hadoop
After watching this video, you will be able to identify key features of relational database management system (RDBMS) and Hadoop.
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Predictive Analytics: Common Data Sources
After watching this video, you will be able to identify common sources of data.
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Predictive Analytics: Extract, Transform, and Load Data
After watching this video, you will be able to recognize key concepts of extracting, transforming, and loading data.
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Predictive Analytics: Data Collection Considerations
After watching this video, you will be able to identify important aspects of data collection.
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Predictive Analytics: Data Collection Strategy
After watching this video, you will be able to recognize elements of data collection strategy.
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Predictive Analytics: Data Exploration Objectives
After watching this video, you will be able to recognize various ways exploration helps in understanding data.
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Predictive Analytics: Prescriptive Data Analytics
After watching this video, you will be able to recognize key features of prescriptive analytics.
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Predictive Analytics: What Is Data Mining?
After watching this video, you will be able to recognize key features of data mining.
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Predictive Analytics: Data Exploration Roadmap
After watching this video, you will be able to list key steps in the data exploration roadmap.
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Predictive Analytics: Descriptive Data Analytics
After watching this video, you will be able to recognize key features of descriptive analytics.
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Predictive Analytics: Correlation and Predictive Analytics
After watching this video, you will be able to recognize how correlation is used in predictive analytics.
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Predictive Analytics: Correlation and Causation
After watching this video, you will be able to recognize when correlation indicates causation and when it doesn't.
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Predictive Analytics: Multivariate Analysis: Testing Goodness-of-Fit
After watching this video, you will be able to determine if a data sample is representative of the data population.
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Predictive Analytics: Overview of Correlation
After watching this video, you will be able to identify key features of correlation.
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Predictive Analytics: Statistical Significance of Correlation
After watching this video, you will be able to distinguish between statistical versus practical significance.
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Predictive Analytics: Introduction to Regression Analysis
After watching this video, you will be able to recognize the role of regression in predictive analytics.
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Predictive Analytics: Best Fit and Residual Analysis
After watching this video, you will be able to identify how the predictive power of a model can be assessed using the r-squared metric.
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Predictive Analytics: Choosing Predictive Data
After watching this video, you will be able to identify what data is valuable for prediction.
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Predictive Analytics: Timing and Quantity of Data
After watching this video, you will be able to recognize the different data time frames and quantity of data needed to build predictive models.
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Predictive Analytics: Logistic Regression for Predictive Analytics
After watching this video, you will be able to recognize key features of logistic regression.
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Predictive Analytics: Identify the Regression Technique
After watching this video, you will be able to identify the correct regression technique to use in a given situation.
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Predictive Analytics: Data Measurement Scales
After watching this video, you will be able to recognize features of data measurement scales.
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Predictive Analytics: Descriptive vs. Inferential Statistics
After watching this video, you will be able to recognize features of descriptive and inferential statistics.
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Predictive Analytics: Predictive Analytics and Statistics
After watching this video, you will be able to recognize the role of statistics in predictive analytics.
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Predictive Analytics: Types of Data
After watching this video, you will be able to recognize attributes of qualitative, quantitative, discrete, and continuous data.
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Predictive Analytics: Conditional Probability and Bayes Rule
After watching this video, you will be able to apply Bayes theorem in a given situation.
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Predictive Analytics: Probability Overview and Probabilistic Events
After watching this video, you will be able to recognize basic features of probability and the types of probabilistic events.
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Predictive Analytics: Addition and Multiplication Rules
After watching this video, you will be able to apply addition and multiplication rules for a probabilistic event.
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Predictive Analytics: Univariate Analysis: CI for Hypothesis Testing
After watching this video, you will be able to recognize how confidence intervals (CI) are used for hypothesis testing.
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Predictive Analytics: Multivariate Analysis: Testing for Differences
After watching this video, you will be able to recognize key features of testing for differences in mean and testing for differences in proportion.
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Predictive Analytics: Permutations and Combinations
After watching this video, you will be able to distinguish between permutations and combinations.
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Predictive Analytics: Univariate Analysis: Minimizing the Margin of Error
After watching this video, you will be able to recognize how to reduce the margin of error.
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Predictive Analytics: Government and Crime Prevention
After watching this video, you will be able to illustrate a government or academia application of predictive analytics.
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Predictive Analytics: The Predictive Analytics Project
After watching this video, you will be able to list features of a predictive analytics project.
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Predictive Analytics: Banking and Insurance
After watching this video, you will be able to illustrate a banking or insurance application of predictive analytics.
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Predictive Analytics: Technology and Healthcare
After watching this video, you will be able to illustrate a technology or healthcare application of predictive analytics.
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Predictive Analytics: Collecting and Preparing Data
After watching this video, you will be able to recognize important considerations for preparing data to build a model.
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Predictive Analytics: Building and Training a Predictive Model
After watching this video, you will be able to outline the process of building and training a predictive model.
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Predictive Analytics: Identifying Project Stakeholders and Roles
After watching this video, you will be able to identify project stakeholders and roles.
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Predictive Analytics: Project Requirements and Considerations
After watching this video, you will be able to identify predictive analytics and project considerations.
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Predictive Analytics: Monitoring Model Usefulness and Applying Knowledge
After watching this video, you will be able to recognize important considerations for model usefulness and key features of knowledge application.
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Predictive Analytics: Identify the Type of Analytics
After watching this video, you will be able to recognize the type of analytics presented in examples.
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Predictive Analytics: Predictive Analytics Implementation
After watching this video, you will be able to identify key considerations and tools for predictive analytics implementation.
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Predictive Analytics: Distance and Weight Measures for Numeric Attributes
After watching this video, you will be able to recognize distance and weighted distance measures.
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Predictive Analytics: Proximity Measures for Non-numeric Attributes
After watching this video, you will be able to recognize proximity measures for non-numeric attributes.
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Predictive Analytics: Overview of the k-NN Algorithm
After watching this video, you will be able to recognize features of the k-NN algorithm.
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Predictive Analytics: Basic Artificial Neural Networks
After watching this video, you will be able to recognize steps and considerations to building artificial neural networks.
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Predictive Analytics: Advanced Artificial Neural Network Concepts
After watching this video, you will be able to recognize the purpose of nonlinear activation functions and methods to find the global minimum SSE.
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Predictive Analytics: Implementing the k-NN Algorithm
After watching this video, you will be able to implement the k-NN algorithm.
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Predictive Analytics: Overview of Artificial Neural Networks
After watching this video, you will be able to identify key features of artificial neural networks.
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Predictive Analytics: Important Parameters for Artificial Neural Networks
After watching this video, you will be able to recognize important parameters for artificial neural networks.
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Predictive Analytics: Implementing an Artificial Neural Network
After watching this video, you will be able to implement an artificial neural network.
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Analyzing Big Data with Microsoft R: Features of the RevoScaleR Package
After watching this video, you will be able to list key features of the RevoScaleR package.
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Analyzing Big Data with Microsoft R: Introduction to the R Language
After watching this video, you will be able to describe the R language and its key features.
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Analyzing Big Data with Microsoft R: R Objects and Attributes
After watching this video, you will be able to recognize what R objects and attributes are.
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Analyzing Big Data with Microsoft R: Vectors and Factors
After watching this video, you will be able to describe concepts of R vectors, lists, and factors.
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Analyzing Big Data with Microsoft R: Matrices and Arrays
After watching this video, you will be able to identify important features of R matrices and arrays.
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Analyzing Big Data with Microsoft R: Data Frames and Lists
After watching this video, you will be able to recognize the concept of data frames and lists in R.
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Analyzing Big Data with Microsoft R: R Data Frames vs. Matrices
After watching this video, you will be able to list differences between data frames and matrices.
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Analyzing Big Data with Microsoft R: Introduction to sqlrutils
After watching this video, you will be able to identify important functions available through the sqlrutils package and their use cases.
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Analyzing Big Data with Microsoft R: Introduction to mrsdeploy
After watching this video, you will be able to recognize use cases of the mrsdeploy package and its important functions.
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Analyzing Big Data with Microsoft R: Introduction to olapR
After watching this video, you will be able to describe the olapR package and its important functions.
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Analyzing Big Data with Microsoft R: Introduction to R Packages
After watching this video, you will be able to recognize important R packages and their functions.
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Analyzing Big Data with Microsoft R: Introduction to Microsoft R Packages
After watching this video, you will be able to list important packages available through Microsoft R.
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Analyzing Big Data with Microsoft R: Introduction to RevoScaleR
After watching this video, you will be able to recognize what the RevoScaleR package is.
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Analyzing Big Data with Microsoft R: RevoScaleR Data Analysis Functions
After watching this video, you will be able to identify important functions available in the RevoScaleR package for data analysis.
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Analyzing Big Data with Microsoft R: RevoScaleR Utility and Compute Functions
After watching this video, you will be able to list important functions available in the RevoScaleR package.
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Analyzing Big Data with Microsoft R: Microsoft R vs. R Functions
After watching this video, you will be able to identify similar RevoScaleR Functions and Base R functions.
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Analyzing Big Data with Microsoft R: Introduction to MicrosoftML
After watching this video, you will be able to describe the MicrosoftML package and its important functions.
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Hosting: Hosting Services in IIS
After watching this video, you will be able to host WCF services in Internet Information Services (IIS).
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Hosting: Hosting Environment
After watching this video, you will be able to describe how to choose a hosting environment.
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Hosting: Hosting Options
After watching this video, you will be able to list hosting options available in WCF.
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Hosting: Hosting Services in WAS
After watching this video, you will be able to host WCF services in Windows Process Activation Service (WAC).
-
Hosting: Hosting in a Managed Application
After watching this video, you will be able to describe how to host WCF services in a managed application.
-
Hosting: Hosting Services in a Managed Windows Service
After watching this video, you will be able to recall how to host WCF services in a Managed Windows Service.
-
Hosting: Exercise: Implementing Hosting in WCF
After watching this video, you will be able to implement hosting in WCF.
-
Hosting: Using Configuration-based Activation
After watching this video, you will be able to demonstrate how a WCF service is hosted under IIS or WAS without using a .SVC file.
-
Optimize & Validate Models in Azure Machine Learning Studio: Regression Models
After watching this video, you will be able to describe the metrics for regression models in Azure Machine Learning Studio.
-
Optimize & Validate Models in Azure Machine Learning Studio: Classification Models
After watching this video, you will be able to describe the metrics for classification models in Azure Machine Learning Studio.
-
Microsoft Cognitive Toolkit & Azure Machine Learning: Build a Neural Network
After watching this video, you will be able to build a neural network using Microsoft Cognitive Toolkit.
-
Microsoft Cognitive Toolkit & Azure Machine Learning: Neural Network Overview
After watching this video, you will be able to describe when to implement a neural network.
-
Microsoft Cognitive Toolkit & Azure Machine Learning: Configure a Two-class Neural Network
After watching this video, you will be able to configure a two-class neural network with Azure Machine Learning Studio.
-
Microsoft Cognitive Toolkit & Azure Machine Learning: Two-class Neural Network
After watching this video, you will be able to describe two-class neural networks.
-
Optimize & Validate Models in Azure Machine Learning Studio: Exercise: Optimize and Validate Models
After watching this video, you will be able to optimize and validate models in Azure Machine Learning Studio.
-
Optimize & Validate Models in Azure Machine Learning Studio: Clustering Models
After watching this video, you will be able to describe the metrics for clustering models in Azure Machine Learning Studio.
-
Microsoft Cognitive Toolkit & Azure Machine Learning: Microsoft Cognitive Toolkit (CNTK) for Python
After watching this video, you will be able to install Microsoft Cognitive Toolkit (CNTK) for use in Python.
-
Microsoft Cognitive Toolkit & Azure Machine Learning: Microsoft Cognitive Toolkit (CNTK)
After watching this video, you will be able to describe Microsoft Cognitive Toolkit (CNTK).
-
Microsoft Cognitive Toolkit & Azure Machine Learning: Multiclass Neural Network
After watching this video, you will be able to describe multiclass neural networks.
-
Microsoft Cognitive Toolkit & Azure Machine Learning: Feed Forward Neural Network
After watching this video, you will be able to build and train a feed forward neural network.
-
Microsoft Cognitive Toolkit & Azure Machine Learning: Configure a Multiclass Neural Network
After watching this video, you will be able to configure a multiclass neural network with Azure Machine Learning Studio.
-
Azure AI Gallery & Azure Machine Learning: Create a New Deployment using Azure AI Gallery
After watching this video, you will be able to deploy a solution from Azure AI Gallery.
-
Azure AI Gallery & Azure Machine Learning: Create a New Experiment from an Example
After watching this video, you will be able to create a new experiment using an example from Azure AI Gallery.
-
Azure AI Gallery & Azure Machine Learning: Use Sample Datasets in Azure Machine Learning Studio
After watching this video, you will be able to use sample datasets in Azure Machine Learning Studio.
-
Microsoft Cognitive Toolkit & Azure Machine Learning: Exercise: Build Neural Networks
After watching this video, you will be able to build neural networks using Microsoft Cognitive Toolkit.
-
Microsoft Cognitive Toolkit & Azure Machine Learning: N-Series Virtual Machines
After watching this video, you will be able to create and use Azure N-Series Virtual Machines designed for graphics and visualization workloads.
-
Azure AI Gallery & Azure Machine Learning: Import Experiment from Azure AI Gallery
After watching this video, you will be able to import an experiment from Azure AI Gallery and from within Azure Machine Learning Studio.
-
Azure AI Gallery & Azure Machine Learning: Overview of Azure AI Gallery
After watching this video, you will be able to describe Azure AI Gallery.
-
Azure AI Gallery & Azure Machine Learning: Create a Collection in Azure AI Gallery
After watching this video, you will be able to create a collection in Azure AI Gallery.
-
Azure AI Gallery & Azure Machine Learning: Jupyter Notebooks from Azure AI Gallery
After watching this video, you will be able to download Jupyter notebooks from Azure AI Gallery directly from the Gallery or from Azure Machine Learning Studio.
-
Azure AI Gallery & Azure Machine Learning: Custom Modules from Azure AI Gallery
After watching this video, you will be able to import custom modules from Azure AI Gallery and from within Azure Machine Learning Studio.
-
Azure AI Gallery & Azure Machine Learning: Use Tutorials in Azure AI Gallery
After watching this video, you will be able to use Tutorials in Azure AI Gallery.
-
Virtual Machines & HDInsight: Create a CentOS Data Science VM on Azure
After watching this video, you will be able to create a CentOS data science VM on Azure.
-
Virtual Machines & HDInsight: Create an Ubuntu Data Science VM on Azure
After watching this video, you will be able to create an Ubuntu data science VM on Azure.
-
Azure AI Gallery & Azure Machine Learning: Cortana Intelligence Competitions
After watching this video, you will be able to describe Cortana Intelligence Competitions.
-
Azure AI Gallery & Azure Machine Learning: Use Custom Modules from Azure AI Gallery
After watching this video, you will be able to use a custom module from Azure AI Gallery in a new or existing experiment in Azure Machine Learning Studio.
-
Virtual Machines & HDInsight: Create a Windows Data Science VM on Azure
After watching this video, you will be able to create a Windows data science VM on Azure.
-
Azure AI Gallery & Azure Machine Learning: Exercise: Use Azure AI Gallery
After watching this video, you will be able to use Azure AI Gallery.
-
Virtual Machines & HDInsight: Choose an Appropriate Cluster Type in HDInsight
After watching this video, you will be able to describe the types of clusters in HDInsight and determine how to choose the appropriate one.
-
Virtual Machines & HDInsight: Create a Geo AI Data Science VM on Azure
After watching this video, you will be able to create a Geo AI data science VM on Azure.
-
Virtual Machines & HDInsight: Create a Spark Cluster using HDInsight
After watching this video, you will be able to build and use Machine Learning models with Spark on HDInsight.
-
Virtual Machines & HDInsight: Exploratory Data Analysis with Spark SQL
After watching this video, you will be able to describe Exploratory Data Analysis (EDA) with Spark SQL.
-
Virtual Machines & HDInsight: Create a Deep Learning VM on Azure
After watching this video, you will be able to create a deep learning VM on Azure.
-
SQL Server & Azure Machine Learning: SQL Server VM
After watching this video, you will be able to describe SQL Server VM.
-
Virtual Machines & HDInsight: R Server Overview
After watching this video, you will be able to describe R Server on HDInsight.
-
Virtual Machines & HDInsight: Run MapReduce Jobs on HDInsight Clusters
After watching this video, you will be able to build and use Machine Learning models using MapReduce on HDInsight.
-
Virtual Machines & HDInsight: Exercise: Use HDInsight on Azure
After watching this video, you will be able to use HDInsight on Azure on Azure.
-
Virtual Machines & HDInsight: R Server on HDInsight
After watching this video, you will be able to build and use Machine Learning models using R Server on HDInsight.
-
SQL Server & Azure Machine Learning: Enable SQL Server Machine Learning Services
After watching this video, you will be able to enable SQL Server Machine Learning Services in an Azure SQL Server 2017 VM.
-
SQL Server & Azure Machine Learning: SQL Server Machine Learning Services
After watching this video, you will be able to describe SQL Server Machine Learning Services.
-
SQL Server & Azure Machine Learning: SQL Server and R Security
After watching this video, you will be able to describe the security architecture used for SQL Server and R.
-
SQL Server & Azure Machine Learning: R Interoperability
After watching this video, you will be able to describe R interoperability.
-
SQL Server & Azure Machine Learning: Create a SQL Server VM with PowerShell
After watching this video, you will be able to create a SQL Server VM with PowerShell.
-
SQL Server & Azure Machine Learning: Create a SQL Server VM in the Azure Portal
After watching this video, you will be able to create a SQL Server VM in the Azure portal.
-
SQL Server & Azure Machine Learning: Prepare a Data Science VM for SQL and R
After watching this video, you will be able to prepare data in a SQL Server 2017 virtual machine for use with SQL and R.
-
SQL Server & Azure Machine Learning: R Code in Transact-SQL
After watching this video, you will be able to use R code in Transact-SQL.
-
SQL Server & Azure Machine Learning: Exercise: Use SQL Server R Services
After watching this video, you will be able to use SQL Server R Services.
-
SQL Server & Azure Machine Learning: Summarize Data Using SQL and R
After watching this video, you will be able to install and use Microsoft R Client to summarize data using SQL and R.
-
Transforming Data in Azure Machine Learning Studio: Exercise: Performing Data Transformation
After watching this video, you will be able to transform data using Azure Machine Learning Studio.
-
Using Algorithms in Azure Machine Learning Studio: Supervised Learning Algorithms
After watching this video, you will be able to describe supervised learning algorithms in Azure Machine Learning.
-
Using Algorithms in Azure Machine Learning Studio: Azure Machine Learning Algorithms
After watching this video, you will be able to use the Microsoft Azure Machine Learning Algorithm Cheat Sheet .
-
Using Algorithms in Azure Machine Learning Studio: Text Analytics
After watching this video, you will be able to create a text analytics model in Azure Machine Learning Studio.
-
Using Algorithms in Azure Machine Learning Studio: Linear Regression
After watching this video, you will be able to create a linear regression experiment in Azure Machine Learning Studio.
-
Using Algorithms in Azure Machine Learning Studio: Ensemble Classifier
After watching this video, you will be able to build an Ensemble Classifier in Azure Machine Learning Studio.
-
Using Algorithms in Azure Machine Learning Studio: Reinforcement Learning Algorithms
After watching this video, you will be able to describe reinforcement learning algorithms in Azure Machine Learning.
-
Using Algorithms in Azure Machine Learning Studio: Unsupervised Learning Algorithms
After watching this video, you will be able to describe unsupervised learning algorithms in Azure Machine Learning.
-
Using Algorithms in Azure Machine Learning Studio: Data Clustering
After watching this video, you will be able to cluster labeled and unlabeled data in Azure Machine Learning Studio.
-
Using Algorithms in Azure Machine Learning Studio: Algorithm Considerations
After watching this video, you will be able to describe the considerations when choosing an algorithm in Azure Machine Learning Studio.
-
Optimize & Validate Models in Azure Machine Learning Studio: Split Data
After watching this video, you will be able to use the Split Data module to divide your dataset in Azure Machine Learning Studio.
-
Using Algorithms in Azure Machine Learning Studio: Exercise: Algorithms for Azure Machine Learning
After watching this video, you will be able to choose algorithms for Azure Machine Learning.
-
Optimize & Validate Models in Azure Machine Learning Studio: Stacking Method
After watching this video, you will be able to use the stacking method to build an ensemble in Azure Machine Learning Studio.
-
Optimize & Validate Models in Azure Machine Learning Studio: Sample Data
After watching this video, you will be able to use the Partition and Sample module to sample or partition your dataset in Azure Machine Learning Studio.
-
Optimize & Validate Models in Azure Machine Learning Studio: Evaluate Recommender
After watching this video, you will be able to use the Evaluate Recommender module to measure accuracy of predictions in Azure Machine Learning Studio.
-
Optimize & Validate Models in Azure Machine Learning Studio: Cross Validate Model
After watching this video, you will be able to use the Cross Validate Model module to divide your data into partitions in Azure Machine Learning Studio.
-
Optimize & Validate Models in Azure Machine Learning Studio: Optimize Hyperparameters
After watching this video, you will be able to describe hyperparameters and the two types of methods used in Azure Machine Learning Studio.
-
Optimize & Validate Models in Azure Machine Learning Studio: Optimize Parameters
After watching this video, you will be able to identify the four steps to optimize parameters in Azure Machine Learning Studio.
-
Optimize & Validate Models in Azure Machine Learning Studio: Evaluate Model
After watching this video, you will be able to use the Evaluate Model module to evaluate a trained model in Azure Machine Learning Studio.
-
Optimize & Validate Models in Azure Machine Learning Studio: Parameter Sweep
After watching this video, you will be able to use the Tune Model Hyperparameters module to perform a parameter sweep in Azure Machine Learning Studio.
-
Security Architecture and Engineering: Information System Security Capabilities
After watching this video, you will be able to compare security capabilities of various information systems.
-
Security Architecture and Engineering: Security Control Selection
After watching this video, you will be able to select appropriate security controls based on systems security requirements.
-
Security Architecture and Engineering: Exercise: Describing Security Engineering and Design
After watching this video, you will be able to describe security engineering and design.
-
Security Architecture and Engineering: Vulnerability Mitigation in Security Design
After watching this video, you will be able to mitigate vulnerabilities in security architectures and designs.
-
Security Architecture and Engineering: Fundamental Concepts of Security Models
After watching this video, you will be able to compare various security models.
-
Security Architecture and Engineering: Secure Design Principles in Engineering Processes
After watching this video, you will be able to describe engineering processes using secure design principles.
-
Vulnerability Assessment & Mitigation: Web-based System Vulnerabilities
After watching this video, you will be able to assess vulnerabilities in web-based systems.
-
Vulnerability Assessment & Mitigation: Vulnerability Mitigation for Web-based Systems
After watching this video, you will be able to reduce security vulnerabilities in various web-based systems.
-
Vulnerability Assessment & Mitigation: Common Attacks on Web-based Systems
After watching this video, you will be able to describe common web-based attacks.
-
Vulnerability Assessment & Mitigation: Vulnerability Mitigation for Embedded Devices
After watching this video, you will be able to reduce various embedded device vulnerabilities.
-
Vulnerability Assessment & Mitigation: Common Threats to Embedded Devices
After watching this video, you will be able to describe and compare common threats to embedded devices.
-
Vulnerability Assessment & Mitigation: Exercise: Describing Vulnerability Mitigation
After watching this video, you will be able to describe how to assess and mitigate systems vulnerabilities.
-
Vulnerability Assessment & Mitigation: Mobility Security and Privacy Concerns
After watching this video, you will be able to describe issues related to mobile security and privacy.
-
Vulnerability Assessment & Mitigation: Enterprise Mobility Management
After watching this video, you will be able to define enterprise mobility management.
-
Vulnerability Assessment & Mitigation: Embedded Device Vulnerabilities
After watching this video, you will be able to assess vulnerabilities in embedded devices.
-
Vulnerability Assessment & Mitigation: Vulnerability Mitigation for Mobile Systems
After watching this video, you will be able to reduce security vulnerabilities in mobile systems.
-
Security Assessment & Testing: Provisioning and Protecting Resources
After watching this video, you will be able to describe resource provisioning and protection.
-
Security Assessment & Testing: Supporting Investigations
After watching this video, you will be able to specify steps that can be taken to support investigations.
-
Security Assessment & Testing: Testing Security Controls and Processes
After watching this video, you will be able to implement various tests of security controls and processes.
-
Security Assessment & Testing: Performing Logging and Monitoring Operations
After watching this video, you will be able to conduct logging and monitoring operations.
-
Security Operations: Information Life Cycle
After watching this video, you will be able to 6.5 Conduct or facilitate security audits.
-
Security Operations: Security Principles for Operations
After watching this video, you will be able to recall various operations security principles.
-
Security Assessment & Testing: Performing Security Audits
After watching this video, you will be able to conduct security audits.
-
Security Assessment & Testing: Implementing Audit Strategies
After watching this video, you will be able to design and validate audit strategies.
-
Security Assessment & Testing: Exercise: Assessing and Testing Security
After watching this video, you will be able to describe how to effectively assess and test security.
-
Security Assessment & Testing: Analyzing Test Output
After watching this video, you will be able to analyze test output and generate reports.
-
Security Operations: Configuration Management
After watching this video, you will be able to manage configurations and changes.
-
Security Operations: Asset Management
After watching this video, you will be able to specify asset management controls.
-
Security Operations: Legal Issues
After watching this video, you will be able to describe security-related legal considerations.
-
Security Operations: Privileged Account Management
After watching this video, you will be able to define and manage privileged accounts.
-
Security Operations: Asset Inventory
After watching this video, you will be able to identify asset inventory measures.
-
Security Operations: Exercise: Describing Operations Security Management
After watching this video, you will be able to list key elements of security management of operations.
-
Security Operations: Service Level Agreements
After watching this video, you will be able to define SLAs.
-
Identity and Access Management (IAM): Exercise: Describing IAM
After watching this video, you will be able to describe identity and access management.
-
Identity and Access Management (IAM): Federated Services
After watching this video, you will be able to describe various federated services.
-
Site & Facility Security Controls: Server Rooms and Data Centers
After watching this video, you will be able to identify security controls for server rooms and data centers.
-
Site & Facility Security Controls: Wiring Closets and Intermediate Distribution Areas
After watching this video, you will be able to describe wiring closets and intermediate distribution.
-
Identity and Access Management (IAM): Access Control Models
After watching this video, you will be able to describe various access control models.
-
Identity and Access Management (IAM): Access Review and Provisioning
After watching this video, you will be able to describe access review and provisioning.
-
Identity and Access Management (IAM): Identity Management Implementation
After watching this video, you will be able to recall how to implement identity management.
-
Site & Facility Security Controls: Evidence Storage
After watching this video, you will be able to recall evidence storage techniques and practices.
-
Site & Facility Security Controls: Media Storage Facilities
After watching this video, you will be able to define media storage facilities.
-
Site & Facility Security Controls: Restricted and Work Area Security
After watching this video, you will be able to recognize restricted work area security.
-
Site & Facility Security Controls: Environmental Issues
After watching this video, you will be able to describe environmental controls.
-
Site & Facility Security Controls: Utilities and HVAC
After watching this video, you will be able to describe how to protect utilities and HVAC.
-
Site & Facility Security Controls: Exercise: Describing Facility Management Controls
After watching this video, you will be able to describe various controls for managing facilities.
-
Site & Facility Security Controls: Fire Prevention, Detection, and Suppression
After watching this video, you will be able to recall techniques for fire prevention, detection, and suppression.
-
Identity and Access Management (IAM): Authorization Mechanism Implementation
After watching this video, you will be able to implement and manage authorization mechanisms.
-
Cryptographic Client-based Systems: Integrity and Cryptographic Hashing
After watching this video, you will be able to define integrity and hashing in relation to cryptography.
-
Cryptographic Client-based Systems: Cryptographic Methods
After watching this video, you will be able to describe various cryptographic methods and techniques.
-
Cryptographic Client-based Systems: Overview of Cryptology and Cryptographic Systems
After watching this video, you will be able to describe cryptology and cryptographic systems.
-
Cryptographic Client-based Systems: Cryptographic Life Cycle
After watching this video, you will be able to compare phases of the cryptographic life cycle.
-
Cryptographic Client-based Systems: Cryptanalytic Attacks
After watching this video, you will be able to identify cryptanalytic attacks.
-
Cryptographic Client-based Systems: Digital Signatures
After watching this video, you will be able to define digital signatures.
-
Communication & Network Security: Secure Network Components
After watching this video, you will be able to describe security for network components.
-
Communication & Network Security: Secure Design Principles for Networks
After watching this video, you will be able to identify secure design principles for networking.
-
Communication & Network Security: Multilayer and Converged Protocols
After watching this video, you will be able to compare multilayer and converged protocols.
-
Communication & Network Security: OSI TCP/IP Models
After watching this video, you will be able to define OSI TCP/IP models.
-
Cryptographic Client-based Systems: Key Management Practices
After watching this video, you will be able to recall various key management practices.
-
Cryptographic Client-based Systems: Public Key Infrastructure (PKI)
After watching this video, you will be able to describe the use and function of public key infrastructure.
-
Cryptographic Client-based Systems: Exercise: Describing Cryptographic Techniques
After watching this video, you will be able to describe cryptographic techniques.
-
Cryptographic Client-based Systems: Digital Rights Management (DRM)
After watching this video, you will be able to describe key aspects of digital rights management.
-
Communication & Network Security: NAC and Endpoint Security
After watching this video, you will be able to describe NAC and endpoint security.
-
Communication & Network Security: Transmission Medium
After watching this video, you will be able to define transmission medium.
-
Communication & Network Security: Content-distribution Networks
After watching this video, you will be able to identify content-distribution networks.
-
Identity and Access Management (IAM): Asset Access Control
After watching this video, you will be able to control physical and logical asset access.
-
Communication & Network Security: Exercise: Describing Security for Networks
After watching this video, you will be able to describe key aspects of security for networks.
-
Identity and Access Management (IAM): Identity Integration
After watching this video, you will be able to integrate identity as a third-party service.
-
Identity and Access Management (IAM): Entity Identification and Authentication
After watching this video, you will be able to manage the identification and authentication of entities.
-
Communication & Network Security: Wireless Networking
After watching this video, you will be able to identify types of wireless network.
-
Communication & Network Security: Unified Communications
After watching this video, you will be able to compare various types of unified communications.
-
Communication & Network Security: Virtualized Network Security
After watching this video, you will be able to secure virtualized networks.
-
Communication & Network Security: Remote Access Technology
After watching this video, you will be able to describe remote access technology.
-
Consume Models & APIs Using Azure Machine Learning Studio: Recommendations Solution Template
After watching this video, you will be able to use the Recommendations Solution template to create product recommendations.
-
Consume Models & APIs Using Azure Machine Learning Studio: Consume Language APIs
After watching this video, you will be able to use the Language API to process text.
-
Importing and Exporting in Azure Machine Learning Studio: Importing from Azure Blob Storage
After watching this video, you will be able to import data from Azure Blob storage using the Import Data module in Azure Machine Learning Studio.
-
Consume Models & APIs Using Azure Machine Learning Studio: Exercise: Consuming a Web Service
After watching this video, you will be able to consume an Azure Machine Learning web service.
-
Importing and Exporting in Azure Machine Learning Studio: Importing from Azure Table
After watching this video, you will be able to recall how to import data from Azure tables using Azure Machine Learning Studio's Import Data module.
-
Importing and Exporting in Azure Machine Learning Studio: Importing from a Web Site with HTTP
After watching this video, you will be able to import data from a web site using the Import Data module in Azure Machine Learning Studio.
-
Importing and Exporting in Azure Machine Learning Studio: Exporting to Azure SQL Database
After watching this video, you will be able to work with Azure Machine Learning Studio's Export Data module to export data to Azure SQL Database.
-
Importing and Exporting in Azure Machine Learning Studio: Exporting to Azure Blob Storage
After watching this video, you will be able to export data to Azure Blob storage with the Export Data module of Azure Machine Learning Studio.
-
Importing and Exporting in Azure Machine Learning Studio: Importing from Azure SQL Database
After watching this video, you will be able to use the Import Data module in Azure Machine Learning Studio to import data from Azure SQL Database.
-
Importing and Exporting in Azure Machine Learning Studio: Importing from Hive Queries
After watching this video, you will be able to import data from Hive Queries with Azure Machine Learning Studio's Import Data module.
-
Importing and Exporting in Azure Machine Learning Studio: Importing Data from On-premises SQL Server Database
After watching this video, you will be able to work with Azure Machine Learning Studio's Import Data module to import data from an on-premises SQL Server database.
-
Importing and Exporting in Azure Machine Learning Studio: Exporting Intermediate Data to a Dataset
After watching this video, you will be able to describe how to export data from an experiment to a saved dataset.
-
Importing and Exporting in Azure Machine Learning Studio: Exporting via Hive Queries
After watching this video, you will be able to export data via Hive Queries using Azure Machine Learning Studio's Export Data module.
-
Importing and Exporting in Azure Machine Learning Studio: Exercise: Importing and Exporting Data
After watching this video, you will be able to import and export data using Azure Machine Learning Studio.
-
Summarize Data with Azure Machine Learning Studio: Test Hypothesis Using t-Test Module
After watching this video, you will be able to use the Test Hypothesis Using t-Test module to compare groups of data.
-
Summarize Data with Azure Machine Learning Studio: Pearson's R test
After watching this video, you will be able to use the Compute Linear Correlation module to measure the relationship between two variables.
-
Summarize Data with Azure Machine Learning Studio: Python Notebooks
After watching this video, you will be able to use existing Python notebooks in Azure Machine Learning Studio.
-
Summarize Data with Azure Machine Learning Studio: Microsoft R
After watching this video, you will be able to use existing Microsoft R in Azure Machine Learning Studio.
-
Summarize Data with Azure Machine Learning Studio: Compute Elementary Statistics
After watching this video, you will be able to use the Compute Elementary Statistics module to generate a summary report of your dataset.
-
Summarize Data with Azure Machine Learning Studio: Summarize Data Module
After watching this video, you will be able to use the Summarize Data module to measure your dataset.
-
Summarize Data with Azure Machine Learning Studio: Binning and Grouping
After watching this video, you will be able to use the Group Data into Bins module to bin and group data.
-
Summarize Data with Azure Machine Learning Studio: Group Categorical Values
After watching this video, you will be able to use Group Categorical Values module to group multiple values into new levels.
-
Summarize Data with Azure Machine Learning Studio: External Packages for Python
After watching this video, you will be able to use the Execute Python Script module in Azure Machine Learning Studio.
-
Summarize Data with Azure Machine Learning Studio: Import External R Packages
After watching this video, you will be able to use the Execute R Script module in Azure Machine Learning Studio to install external R packages.
-
Summarize Data with Azure Machine Learning Studio: Exercise: Summarize and Group Data
After watching this video, you will be able to summarize and group data in Azure Machine Learning Studio.
-
Data Cleanup with Azure Machine Learning Studio: Synthetic Minority Oversampling Technique (SMOTE)
After watching this video, you will be able to use the SMOTE module of Azure Machine Learning Studio to increase the number of cases in your dataset.
-
Data Cleanup with Azure Machine Learning Studio: Duplicate Data
After watching this video, you will be able to use the Remove Duplicate Rows module of Azure Machine Learning Studio to remove duplicates from your dataset.
-
Data Cleanup with Azure Machine Learning Studio: Grouping Data
After watching this video, you will be able to use the Group Data into Bins module of Azure Machine Learning Studio to change the distribution of continuous data.
-
Data Cleanup with Azure Machine Learning Studio: Clip Values
After watching this video, you will be able to use the Clip Values module of Azure Machine Learning Studio to detect outliers and rescale numeric data.
-
Data Cleanup with Azure Machine Learning Studio: Counts and Features
After watching this video, you will be able to use the count modules of Azure Machine Learning Studio to summarize the important information in your data set.
-
Data Cleanup with Azure Machine Learning Studio: Applying Filters
After watching this video, you will be able to use the filter modules of Azure Machine Learning Studio to transform your data.
-
Data Cleanup with Azure Machine Learning Studio: Missing Data
After watching this video, you will be able to use the Clean Missing Data module of Azure Machine Learning Studio to remove or replace missing values.
-
Data Cleanup with Azure Machine Learning Studio: SQL Transformations
After watching this video, you will be able to use the Apply SQL Transformation module of Azure Machine Learning Studio to specify a SQL query on your input dataset.
-
Data Cleanup with Azure Machine Learning Studio: Normalization
After watching this video, you will be able to use the Normalize Data module of Azure Machine Learning Studio to transform a dataset.
-
Transforming Data in Azure Machine Learning Studio: Merge datasets
After watching this video, you will be able to use the Join Data module in Azure Machine Learning Studio to merge two datasets.
-
Data Cleanup with Azure Machine Learning Studio: Exercise: Clean Datasets for Learning
After watching this video, you will be able to apply cleanup modules to your datasets in Azure Machine Learning Studio.
-
Transforming Data in Azure Machine Learning Studio: Create Features
After watching this video, you will be able to create features from your data in Azure Machine Learning Studio.
-
Transforming Data in Azure Machine Learning Studio: Indicator Values
After watching this video, you will be able to use the Convert to Indicator Values module in Azure Machine Learning Studio to convert into binary indicator columns.
-
Transforming Data in Azure Machine Learning Studio: Principal Component Analysis (PCA)
After watching this video, you will be able to use the Principal Component Analysis module in Azure Machine Learning Studio to reduce the dimensionality of your data.
-
Transforming Data in Azure Machine Learning Studio: Feature Selection
After watching this video, you will be able to add feature selection to your experiment in Azure Machine Learning Studio.
-
Transforming Data in Azure Machine Learning Studio: Adding Rows
After watching this video, you will be able to use the Add Rows module in Azure Machine Learning Studio to concatenate datasets.
-
Transforming Data in Azure Machine Learning Studio: Adding Columns
After watching this video, you will be able to use the Add Columns module in Azure Machine Learning Studio to concatenate datasets.
-
Transforming Data in Azure Machine Learning Studio: Metadata
After watching this video, you will be able to use the Edit Metadata module in Azure Machine Learning Studio to change the metadata in a dataset.
-
Transforming Data in Azure Machine Learning Studio: Subset of Columns
After watching this video, you will be able to use the Select Columns in Dataset module in Azure Machine Learning Studio to choose a subset of columns within your dataset.
-
Introduction to Azure Machine Learning: What Is Machine Learning?
After watching this video, you will be able to describe Machine Learning in the Microsoft Azure cloud platform.
-
Introduction to Azure Machine Learning: Comparing Azure Machine Learning Studio Algorithms
After watching this video, you will be able to describe the four popular families of algorithms in Azure Machine Learning Studio.
-
Introduction to Azure Machine Learning: What Is Azure Machine Learning Studio?
After watching this video, you will be able to use Azure Machine Learning Studio and identify its capabilities.
-
Introduction to Azure Machine Learning: Preparing Data
After watching this video, you will be able to add the Select Columns in Dataset module to specify which columns you want to include in your model.
-
Introduction to Azure Machine Learning: Getting Data
After watching this video, you will be able to add a sample dataset that's included in your workspace.
-
Introduction to Azure Machine Learning: Defining Features
After watching this video, you will be able to connect the Clean Missing Data module to the Select Columns in Dataset module.
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Introduction to Azure Machine Learning: Managing a Workspace
After watching this video, you will be able to manage your workspace in the Azure portal by inviting users and switching between workspaces.
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Introduction to Azure Machine Learning: Creating a Workspace
After watching this video, you will be able to create an Azure Machine Learning Studio workspace.
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Introduction to Azure Machine Learning: Creating a Jupyter Notebook
After watching this video, you will be able to create a Jupyter notebook.
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Introduction to Azure Machine Learning: Working with Azure Resource Manager (ARM)
After watching this video, you will be able to use an Azure Resource Manager (ARM) deployment template to deploy a workspace.
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Introduction to Azure Machine Learning: Scoring and Evaluating
After watching this video, you will be able to use the Score Model and Evaluate Model modules to determine how well your model functions.
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Introduction to Azure Machine Learning: Splitting Data and Applying ML Algorithms
After watching this video, you will be able to split the data for ML training, testing, and evaluation, and apply a learning algorithm to your model.
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Introduction to Azure Machine Learning: Exercise: Creating an Experiment
After watching this video, you will be able to create an experiment in Azure Machine Learning Studio.
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Introduction to Azure Machine Learning: Using the R Programming Language
After watching this video, you will be able to work with the R programming language within Azure Machine Learning Studio.
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Deploying Models with Azure Machine Learning Studio: Using Import and Export Modules
After watching this video, you will be able to create a predictive experiment using import and export modules.
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Deploying Models with Azure Machine Learning Studio: Testing a Web Service
After watching this video, you will be able to test your web service in both Azure Machine Learning Studio and the Azure Machine Learning Web Service Management portal.
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Deploying Models with Azure Machine Learning Studio: Converting to a Predictive Experiment
After watching this video, you will be able to convert your training experiment into a predictive experiment.
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Deploying Models with Azure Machine Learning Studio: Preparing an Experiment for Deployment
After watching this video, you will be able to prepare and streamline an experiment in preparation for deployment as a predictive analytic solution.
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Deploying Models with Azure Machine Learning Studio: Deploying a New Web Service
After watching this video, you will be able to deploy an experiment as a New web service.
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Deploying Models with Azure Machine Learning Studio: Deploying a Classic Web Service
After watching this video, you will be able to deploy your experiment as a Classic web service.
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Deploying Models with Azure Machine Learning Studio: Using Language Understanding Intelligent Service
After watching this video, you will be able to create and publish a language understanding model using Language Understanding Intelligent Service (LUIS).
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Deploying Models with Azure Machine Learning Studio: Using Train Matchbox Recommender
After watching this video, you will be able to use the Train Matchbox Recommender to train a recommendation module.
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Consume Models & APIs Using Azure Machine Learning Studio: Authorization Key
After watching this video, you will be able to obtain an Azure Machine Learning authorization key.
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Deploying Models with Azure Machine Learning Studio: Exercise: Deploying a Web Service
After watching this video, you will be able to deploy an Azure Machine Learning web service.
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Deploying Models with Azure Machine Learning Studio: Deploying Web Services with Web Service Parameters
After watching this video, you will be able to deploy a web service using Web Service Parameters in Azure Machine Learning Studio.
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Consume Models & APIs Using Azure Machine Learning Studio: Excel Add-in for Web Service
After watching this video, you will be able to use the Excel add-in for your Azure Machine Learning web service.
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Consume Models & APIs Using Azure Machine Learning Studio: Request-Response Service (RRS)
After watching this video, you will be able to use a Request-Response Service (RRS) to consume a published Azure Machine Learning model.
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Consume Models & APIs Using Azure Machine Learning Studio: Web Service Connection
After watching this video, you will be able to connect to a web service in Azure Machine Learning Studio.
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Consume Models & APIs Using Azure Machine Learning Studio: Web Services from Excel
After watching this video, you will be able to call a web service from Excel.
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Consume Models & APIs Using Azure Machine Learning Studio: Batch Execution Service
After watching this video, you will be able to use a Batch Execution Service to consume a published Azure Machine Learning model.
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Consume Models & APIs Using Azure Machine Learning Studio: Batch Execution Service Web App Templates
After watching this video, you will be able to use a Batch Execution Service template to consume a web service.
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Consume Models & APIs Using Azure Machine Learning Studio: Request-Response Service Web App Templates
After watching this video, you will be able to use a Request-Response Service template to consume a web service.
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Consume Models & APIs Using Azure Machine Learning Studio: Computer Vision APIs
After watching this video, you will be able to call the Computer Vision API to analyze an image and retrieve metadata.
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Consume Models & APIs Using Azure Machine Learning Studio: Publishing to Azure AI Gallery
After watching this video, you will be able to publish your Azure Machine Learning Model to the Azure AI Gallery.
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Consume Models & APIs Using Azure Machine Learning Studio: REST APIs
After watching this video, you will be able to describe Azure Machine Learning Studio REST APIs.
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Consume Models & APIs Using Azure Machine Learning Studio: Azure API Management
After watching this video, you will be able to describe Azure API Management.