COURSE OUTLINE

Lesson 1: Solving Business Problems Using AI and ML

→ Topic A: Identify AI and ML Solutions for
Business Problems
• The Data Hierarchy—Making Data Useful
• Big Data
• Guidelines for Working with Big Data
• Data Mining
• Examples of Applied AI and ML in Business
• Guidelines to Select Appropriate Business Applications for AI and ML

Activity: Identifying Appropriate Business Applications for AI and ML

→ Topic B: Follow a Machine Learning
Workflow
• Machine Learning Model
• Machine Learning Workflow
• Data Science Skillset
• Traditional IT Skillsets
• Concept Drift
• Transfer Learning
• Guidelines for Following the Machine Learning Workflow

Activity: Planning the Machine Learning Workflow

→ Topic C: Formulate a Machine Learning Problem
• Problem Formulation
• Framing a Machine Learning Problem
• Differences Between Traditional Programming and Machine Learning
• Differences Between Supervised and Unsupervised Learning
• Randomness in Machine Learning
• Uncertainty
• Random Number Generation
• Machine Learning Outcomes
• Guidelines for Formulating a Machine Learning Outcome

Activity: Selecting a Machine Learning Outcome

→ Topic D: Select Appropriate Tools
• Open Source AI Tools
• Proprietary AI Tools
• New Tools and Technologies
• Hardware Requirements
• GPUs vs. CPUs
• GPU Platforms
• Cloud Platforms
• Guidelines for Configuring a Machine Learning Toolset
• How to Install Anaconda Activity: Selecting a Machine Learning Toolset

Lesson 2: Collecting and Refining the Dataset

→ Topic A: Collect the Dataset
• Machine Learning Datasets
• Structure of Data
• Terms Describing Portions of Data
• Data Quality Issues
• Data Sources
• Open Datasets
• Guidelines for Selecting a Machine Learning Dataset

Activity: Examining the Structure of a Machine Learning Dataset

• Extract, Transform, and Load (ETL)
• Machine Learning Pipeline
• ML Software Environments
• Guidelines for Loading a Dataset

Activity: Loading the Dataset

→ Topic B: Analyze the Dataset to Gain Insights
• Dataset Structure
• Guidelines for Exploring the Structure of a Dataset

Activity: Exploring the General Structure of the Dataset

• Normal Distribution
• Non-Normal Distributions
• Descriptive Statistical Analysis
• Central Tendency
• When to Use Different Measures of Central Tendency
• Variability
• Range Measures
• Variance and Standard Deviation
• Calculation of Variance
• Variance in a Sample Set
• Calculation of Standard Deviation
• SkewnessCalculation of Skewness Measures
• Kurtosis
• Calculation of Kurtosis
→ Statistical Moments
→ Correlation Coefficient
→ Calculation of Pearson’s Correlation Coefficient
→ Guidelines for Analyzing a Dataset

Activity: Analyzing a Dataset Using Statistical Measures

→ Topic C: Use Visualizations to Analyze Data
• Visualizations
• Histogram
• Box Plot
• Scatterplot
• Geographical Maps
• Heat Maps
• Guidelines for Using Visualizations to Analyze Data

Activity: Analyzing a Dataset Using Visualizations

→ Topic D: Prepare Data
• Data Preparation
• Data Types
• Operations You Can Perform on Different Types of Data
• Continuous vs. Discrete Variables
• Data Encoding
• Dimensionality Reduction
• Impute Missing Values
• Duplicates
• Normalization and Standardization
• Summarization
• Holdout Method

Activity: Guidelines for Preparing Training and Testing Data

→ Splitting the Training and Testing Datasets and Labels

Lesson 3: Setting Up and Training a Model

Topic A: Set Up a Machine Learning Model
• Design of Experiments
• Hypothesis
• Hypothesis Testing
• Hypothesis Testing Methods
• p-value
• Confidence Interval• Machine Learning Algorithms
• Algorithm Selection
• Guidelines for Setting Up a Machine Learning Model

Activity: Setting Up a Machine Learning Model

→ Topic B: Train the Model
• Iterative Tuning
• Bias
• Compromises
• Model Generalization
• Cross-Validation
• k-Fold Cross-Validation
• Leave-p-Out Cross-Validation

Activity: Dealing with Outliers

• Feature Transformation
• Transformation Functions
• Scaling and Normalizing Features
• The Bias–Variance Tradeoff
• Parameters
• Regularization
• Models in Combination
• Processing Efficiency
• Guidelines for Training and Tuning the Model

Activity: Refitting and Testing the Model

Lesson 4: Finalizing a Model

→ Topic A: Translate Results into Business Actions
• Know Your Audience
• Visualization for Presentation
• Guidelines for Presenting Your Findings

Activity: Translating Results into Business Actions

→ Topic B: Incorporate a Model into a Long-Term Business Solution
• Put a Model into Production
• Production Algorithms
• Pipeline Automation
• Testing and Maintenance
• Consumer-Oriented Applications
• Guidelines for Incorporating Machine Learning into a Long-Term Solution

Activity: Incorporating a Model into a Long-Term Solution

Lesson 5: Building Linear Regression Models

→ Topic A: Build a Regression
Model Using Linear Algebra
• Linear Regression
• Linear Equation
• Linear Equation Data Example
• Straight Line Fit to Example Data
• Linear Equation Shortcomings
• Linear Regression in Machine Learning
• Linear Regression in Machine Learning
Example
• Matrices in Linear Regression
• Normal Equation
• Linear Model with Higher Order Fits
• Linear Model with Multiple Parameters
• Cost Function
• Mean Squared Error (MSE)
• Mean Absolute Error (MAE)
• Coefficient of Determination
• Normal Equation Shortcomings
• Guidelines for Building a Regression
Model Using Linear Algebra
Activity: Building a Regression
Model Using Linear Algebra
→ Topic B: Build a Regularized Regression
Model Using Linear Algebra
• Regularization Techniques
• Ridge Regression
• Lasso Regression
• Elastic Net Regression
• Guidelines for Building a Regularized
Linear Regression Model
Activity: Building a Regularized Linear Regression Model
→ Topic C: Build an Iterative Linear Regression Model
• Iterative Models
• Gradient Descent
• Global Minimum vs. Local Minima
• Learning Rate
• Gradient Descent Techniques
• Guidelines for Building an Iterative Linear Regression Model

Activity: Building an Iterative Linear Regression Model

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Lesson 6: Building Classification Models

→ Topic A: Train Binary
Classification Models
• Linear Regression Shortcomings
• Logistic Regression
• Decision Boundary
• Cost Function for Logistic Regression
• A Simpler Alternative for Classification
• k-Nearest Neighbor (k-NN)
• k Determination
• Logistic Regression vs. k-NN
• Guidelines for Training Binary
Classification Models
Activity: Training Binary
Classification Model
→ Topic B: Train Multi-Class
Classification Models
• Multi-Label Classification
• Multi-Class Classification
• Multinomial Logistic Regression
• Guidelines for Training Multi-Class
Classification Models
Activity: Training a Multi-Class
Classification Model
→ Topic C: Evaluate Classification Models
• Model Performance
• Confusion Matrix
• Classifier Performance Measurement
• Accuracy
• Precision
• Recall
• Precision–Recall Tradeoff
• F1 Score
• Receiver Operating Characteristic (ROC)
Curve
• Thresholds
• Area Under Curve (AUC)
• Precision–Recall Curve (PRC)
• Guidelines for Evaluating Classification
Models
Activity: Evaluating a
Classification Model
→ Topic D: Tune Classification Models
• Hyperparameter Optimization
• Grid Search
• Randomized Search
• Bayesian Optimization
• Genetic Algorithms• Guidelines for Tuning Classification Models

Activity: Tuning a Classification Model

Lesson 7: Building Clustering Models

→ Topic A: Build k-Means Clustering Models
• k-Means Clustering
• Global vs. Local Optimization
• k Determination
• Elbow Point
• Cluster Sum of Squares
• Silhouette Analysis
• Additional Cluster Analysis Methods
• Guidelines for Building a k-Means
Clustering Model
Activity: Building a k-Means
Clustering Model
→ Topic B: Build Hierarchical
Clustering Models
• k-Means Clustering Shortcomings
• Hierarchical Clustering
• Hierarchical Clustering Applied to a Spiral Dataset
• When to Stop Hierarchical Clustering
• Dendrogram
• Guidelines for Building a Hierarchical Clustering Model
Activity: Building a Hierarchical Clustering Model

Lesson 8: Building Advanced Models

→ Topic A: Build Decision Tree Models
• Decision Tree
• Classification and Regression Tree
(CART)
• Gini Index Example
• CART Hyperparameters
• Pruning
• C4.5
• Continuous Variable Discretization
• Bin Determination
• One-Hot Encoding
• Decision Tree Algorithm Comparison
• Decision Trees Compared to Other Algorithms
• Guidelines for Building a Decision Tree ModelActivity: Building a
Decision Tree Model
→ Topic B: Build Random Forest Models
• Ensemble Learning
• Random Forest
• Out-of-Bag Error
• Random Forest Hyperparameters
• Feature Selection Benefits
• Guidelines for Building a Random Forest Model
Activity: Building a Random Forest Model

Lesson 9: Building Support-Vector Machines

→ Topic A: Build SVM Models for
Classification
• Support-Vector Machines (SVMs)
• SVMs for Linear Classification
• Hard-Margin Classification
• Soft-Margin Classification
• SVMs for Non-Linear Classification
• Kernel Trick
• Kernel Trick Example
• Kernel Methods
• Guidelines for Building an SVM Model
Activity: Building an SVM Model
→ Topic B: Build SVM Models for Regression
• SVMs for Regression
• Guidelines for Building SVM Models for Regression
Activity: Building an SVM Model for Regression

 

Lesson 10: Building Artificial Neural Networks

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Topic A: Build Multi-Layer
Perceptrons (MLP)
• Artificial Neural Network (ANN)
• Perceptron
• Multi-Label Classification Perceptron
• Perceptron Training
• Perceptron Shortcomings
• Multi-Layer Perceptron (MLP)
• ANN Layers
• Backpropagation
• Activation Functions
• Guidelines for Building MLPsActivity:
Building an MLP
→ Topic B: Build Convolutional Neural
Networks (CNN)
• Traditional ANN Shortcomings
• Convolutional Neural Network (CNN)
• CNN Filters
• CNN Filter Example
• Padding
• Stride
• Pooling Layer
• CNN Architecture
• Generative Adversarial Network (GAN)
• GAN Architecture
• Guidelines for Building CNNs
Activity: Building a CNN

Lesson 11: Promoting Data Privacy and Ethical Practices

→ Topic A: Protect Data Privacy
• Protected Data
• Obligation to Protect PII
• Relevant Data Privacy Laws
• Privacy by Design
• Data Privacy Principles at Odds with Machine Learning
• Guidelines for Complying with Data Privacy Laws and Standards
• Complying with Applicable Laws and Standards
• Open Source Data Sharing and Privacy• Data Anonymization
• Guidelines for Data Anonymization
• The Big Data Challenge
• Guidelines for Protecting Data Privacy
Activity: Protecting
Data Privacy
→ Topic B: Promote Ethical Practices
• Preconceived Notions
• The Black Box Challenge
• Prejudice Bias
• Proxies for Larger Social Discriminations
• Ethics in NLP
• Guidelines for Promoting Ethical
Practices
Activity: Promoting
Ethical Practices
→ Topic C: Establish Data Privacy and
Ethics Policies
• Privacy and Data Governance for AI and ML
• Intellectual Property
• Humanitarian Principles
• Guidelines for Establishing Policies Covering Data Privacy and Ethics

Activity: Establishing Policies Covering Data Privacy and Ethics

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