Module 1: A Gentle Introduction to Machine Learning

• The data science ecosystem
• Types of data analytics
• Difference between artificial intelligence (AI) and machine learning
• Machine learning types
• ML toolkit

Module 2: The Machine Learning Pipeline

• The stages of machine learning
• Data cleaning strategies
• Qualities of good data
• Statistics for ML

Module 3: Building a Machine Learning Model

• Classification
• Regression
• Clustering

Module 4: Exploratory Data Analysis (EDA)

• Why do we need EDA?
• Methodology
• EDA best practices

Module 5: Feature Selection and Feature Engineering

• Definitions
• Permutation-based feature selection
• Principal component analysis (PCA) and linear discriminant analysis (LDA)

Module 6: Normalization Methodologies

• Linear scaling
• Clipping
• Log scaling
• Z-score

Module 7: Metrics to Evaluate ML Models

• Regression metrics
• Classification metrics
• Ranking

Module 8: Types of ML Algorithms

• Linear and logistic
• Decision tress and random forest
• Support vector machines
• K-means clustering
• Probabilistic AI
• Time-series analysis

Module 9: Optimizing ML Models

• The need for optimization
• Bias and variance trade-of
• Overfitting and underfitting
• Hyperparameter optimization
• L1 and L2 regularization
• Understanding drift