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
– 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
– 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
– 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
– Selecting a Machine Learning Toolset

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
– Examining the Structure of a Machine Learning Dataset
– Extract, Transform, and Load (ETL)
– Machine Learning Pipeline
– ML Software Environments
– Guidelines for Loading a Dataset

– Loading the Dataset

Topic B: Analyze the Dataset to Gain Insights
– Dataset Structure
– Guidelines for Exploring the Structure of a Dataset
– 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
– Skewness
– Calculation of Skewness Measures
– Kurtosis
– Calculation of Kurtosis
– Statistical Moments
– Correlation Coefficient
– Calculation of Pearson’s Correlation Coefficient
– Guidelines for Analyzing a Dataset
– 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
– 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
– Guidelines for Preparing Training and Testing Data
– Splitting the Training and Testing Datasets and Labels

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
– 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
– 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
– Refitting and Testing the Model

Topic A: Translate Results into Business Actions
– Know Your Audience
– Visualization for Presentation
– Guidelines for Presenting Your Findings
– 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
– Incorporating a Model into a Long-Term Solution

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
– 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
– 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
– Building an Iterative Linear Regression Model

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
– 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
– 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
– Evaluating a Classification Model

Topic D: Tune Classification Models
– Hyperparameter Optimization
– Grid Search
– Randomized Search
– Bayesian Optimization
– Genetic Algorithms
– Guidelines for Tuning Classification Models
– Tuning a Classification Model

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
– 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
– Building a Hierarchical Clustering Model

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 Model
– 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
– Building a Random Forest Model

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
– Building an SVM Model

Topic B: Build SVM Models for Regression
– SVMs for Regression
– Guidelines for Building SVM Models for Regression
– Building an SVM Model for Regression

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 MLPs
– 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
– Building a CNN

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
– 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
– 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
– Establishing Policies Covering Data Privacy and Ethics