Lesson 1: Solving Business Problems Using AI and MLTopic A: Identify AI and ML Solutions for Business ProblemsTopic C: Formulate a Machine Learning ProblemTopic D: Select Approaches to Machine Learning
Topic A: Identify AI and ML Solutions for Business Problems
Topic C: Formulate a Machine Learning Problem
Topic D: Select Approaches to Machine Learning
Lesson 2: Preparing DataTopic A: Collect DataTopic B: Transform DataTopic C: Engineer FeaturesTopic D: Work with Unstructured Data
Topic A: Collect Data
Topic B: Transform Data
Topic C: Engineer Features
Topic D: Work with Unstructured Data
Lesson 3: Training, Evaluating, and Tuning a Machine Learning ModelTopic A: Train a Machine Learning ModelTopic B: Evaluate and Tune a Machine Learning Model
Topic A: Train a Machine Learning Model
Topic B: Evaluate and Tune a Machine Learning Model
Lesson 4: Building Linear Regression ModelsTopic A: Build Regression Models Using Linear AlgebraTopic B: Build Regularized Linear Regression ModelsTopic C: Build Iterative 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 ModelsTopic A: Build Univariate Time Series ModelsTopic B: Build Multivariate Time Series 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 NeighborTopic A: Train Binary Classification Models Using Logistic RegressionTopic B: Train Binary Classification Models Using k-Nearest NeighborTopic C: Train Multi-Class Classification ModelsTopic D: Evaluate Classification ModelsTopic E: Tune Classification Models
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 ModelsTopic A: Build k-Means Clustering ModelsTopic B: Build Hierarchical Clustering Models
Topic A: Build k-Means Clustering Models
Topic B: Build Hierarchical Clustering Models
Lesson 8: Building Decision Trees and Random ForestsTopic A: Build Decision Tree ModelsTopic B: Build Random Forest Models
Topic A: Build Decision Tree Models
Topic B: Build Random Forest Models
Lesson 9: Building Support-Vector MachinesTopic A: Build SVM Models for ClassificationTopic B: Build SVM Models for Regression
Topic A: Build SVM Models for Classification
Topic B: Build SVM Models for Regression
Lesson 10: Building Artificial Neural NetworksTopic A: Build Multi-Layer Perceptrons (MLP)Topic B: Build Convolutional Neural Networks (CNN)Topic C: Build Recurrent Neural Networks (RNN)
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 ModelsTopic A: Deploy Machine Learning ModelsTopic B: Automate the Machine Learning Process with MLOpsTopic C: Integrate Models into Machine Learning Systems
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 OperationsTopic A: Secure Machine Learning PipelinesTopic B: Maintain Models in Production
Topic A: Secure Machine Learning Pipelines
Topic B: Maintain Models in Production

The overall data science and machine learning process from end to end: formulating the problem; collecting and preparing data; analyzing data; engineering and preprocessing data; training, tuning, and evaluating a model; and finalizing a model.
Statistical concepts such as sampling, hypothesis testing, probability distribution, randomness, etc.
Summary statistics such as mean, median, mode, interquartile range (IQR), standard deviation, skewness, etc.
Graphs, plots, charts, and other methods of visual data analysis.