Lesson 1: Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning

Lesson 2: Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data

Lesson 4: Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models

Lesson 5: Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models

Lesson 6: Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models

Lesson 7: Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models

Lesson 8: Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models

Lesson 9: Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression

Lesson 10: Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN)

Lesson 11: Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems

Lesson 12: Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production