Machine Learning
Summary
This course provides a comprehensive introduction to Machine Learning, covering both theoretical concepts and practical implementation. Starting with foundational principles and the ML pipeline, students will explore supervised and unsupervised learning, model evaluation, and optimization techniques. Optional deep learning modules provide insights into advanced topics like neural networks and reinforcement learning. Hands-on practice with popular ML tools and frameworks ensures that participants gain real-world skills, culminating in a capstone project to apply their learning to a practical problem. Ethical considerations and best practices are emphasized throughout to promote responsible AI development.
Objectives
Understand the fundamental concepts and types of Machine Learning (ML).
Learn how to prepare and preprocess data for ML models.
Explore key algorithms for supervised and unsupervised learning.
Gain hands-on experience building, training, and evaluating ML models using Python.
Understand model evaluation techniques and how to optimize models effectively.
Develop awareness of ethical considerations in ML, such as bias and fairness.
Apply knowledge to solve real-world problems through a capstone project.
Days: 4+
1. Introduction to Machine Learning
What is Machine Learning?
Definition and core concepts
Machine learning vs. traditional programming
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Applications of ML
Examples: Recommendation systems, Image recognition, Fraud detection
2. The Machine Learning Pipeline
Problem Definition
Identifying use cases
Understanding data requirements
Data Preparation
Collecting, cleaning, and transforming data
Splitting datasets: Training, Validation, Test
Feature Engineering
Feature selection
Feature scaling (normalization, standardization)
3. Supervised Learning
Concepts
Input-output mapping
Regression vs. Classification tasks
Key Algorithms
Linear Regression
Logistic Regression
Decision Trees and Random Forests
Support Vector Machines (SVM)
Evaluation Metrics
Mean Absolute Error (MAE), Mean Squared Error (MSE) for regression
Accuracy, Precision, Recall, F1-score for classification
4. Unsupervised Learning
Concepts
Finding patterns in data
Clustering vs. Dimensionality Reduction
Key Algorithms
K-Means Clustering
Hierarchical Clustering
Principal Component Analysis (PCA)
Applications
Customer segmentation
Anomaly detection
5. Reinforcement Learning (Optional, Advanced)
Core Concepts
Agents, Environment, Actions, Rewards
Exploration vs. Exploitation
Algorithms
Q-Learning
Deep Reinforcement Learning
6. Model Evaluation and Optimization
Model Evaluation
Cross-validation
Confusion matrix
ROC-AUC curve
Hyperparameter Tuning
Grid Search
Random Search
Overfitting and Underfitting
Bias-variance tradeoff
Techniques to prevent overfitting (e.g., regularization, dropout)
7. Introduction to Deep Learning (Optional)
Neural Networks
Structure: Input layer, Hidden layers, Output layer
Activation functions
Key Concepts
Gradient Descent
Backpropagation
Applications
Computer Vision
Natural Language Processing
8. Tools and Frameworks
Programming Languages
Python: scikit-learn, TensorFlow, PyTorch
Data Visualization
Matplotlib, Seaborn
Hands-on Exercises
Building ML models with Jupyter Notebook
Using cloud platforms (Google Colab, AWS Sagemaker)
9. Ethics and Best Practices
Ethics in ML
Bias and fairness
Privacy and security
Best Practices
Reproducibility
Documentation and model explainability
10. Capstone Project
Objective
Build and evaluate a machine learning model for a real-world problem
Examples
Predict house prices using regression
Perform customer segmentation using clustering
Build a spam email classifier