Training on Machine Learning Fundamentals: From Data to Models

Module 1: Introduction to Machine Learning and Problem Framing

• What is machine learning and its applications
• Types of machine learning: supervised, unsupervised, reinforcement
• Framing machine learning problems
• Understanding the machine learning workflow
• Key ML concepts and terminology
• Case Study: Framing a business problem as a machine learning problem

Module 2: Data Preparation and Exploratory Data Analysis (EDA)

• Data collection and understanding
• Data cleaning and preprocessing
• Handling missing data and outliers
• Exploratory data analysis (EDA) techniques
• Data visualization for EDA
• Case Study: Conducting EDA on a real-world dataset

Module 3: Supervised Learning: Regression and Classification

• Introduction to supervised learning
• Linear and logistic regression
• Decision trees and random forests
• Support vector machines (SVM)
• Performance metrics for classification and regression
• Case Study: Building a classification model on a real-world dataset

Module 4: Unsupervised Learning: Clustering and Dimensionality Reduction

• Introduction to unsupervised learning
• K-means clustering and hierarchical clustering
• Principal component analysis (PCA)
• t-SNE for visualization
• Applications of unsupervised learning
• Case Study: Applying clustering to a real-world dataset

Module 5: Model Evaluation, Tuning, and Deployment

• Model evaluation techniques: cross-validation, confusion matrix
• Hyperparameter tuning and optimization
• Feature selection and engineering
• Model deployment considerations
• Monitoring and maintaining ML models
• Case Study: Tuning and evaluating a machine learning model

Module 6: Ensemble Methods and Model Stacking

• Introduction to ensemble methods
• Bagging and Random Forests
• Boosting: AdaBoost, Gradient Boosting, XGBoost
• Model stacking and blending
• Improving performance with ensembles
• Case Study: Building an ensemble model for better performance

Module 7: Handling Imbalanced Data and Bias in ML

• Understanding imbalanced data challenges
• Techniques for handling imbalanced data: resampling, SMOTE
• Bias and fairness in machine learning
• Evaluating and mitigating bias in models
• Building fair and inclusive ML systems
• Case Study: Addressing imbalance and bias in a real-world dataset

Module 8: Feature Engineering and Selection

• Importance of feature engineering
• Feature creation and transformation techniques
• Feature selection methods: filter, wrapper, embedded
• Dimensionality reduction for feature optimization
• Automating feature engineering with AI
• Case Study: Performing feature engineering on a real-world dataset

Module 9: Model Interpretability and Explainability

• Importance of model interpretability
• Interpretable models: linear regression, decision trees
• Post-hoc explanation techniques: SHAP, LIME
• Model-agnostic interpretability methods
• Communicating model insights to stakeholders
• Case Study: Explaining a machine learning model to stakeholders

Module 10: ML System Design and Architecture

• Designing ML systems for production
• ML system components and architecture
• Data pipelines and feature stores
• Model serving and API design
• Monitoring and maintaining ML systems
• Case Study: Designing an ML system for a business use case