4-days Instructor-led

Course Description 

This 4-module course provides a practical and comprehensive introduction to machine learning. It covers foundational theory, model building, algorithm selection, and evaluation techniques. Participants will learn to apply supervised and unsupervised learning algorithms using Python and scikit-learn, working with real-world datasets. The course emphasizes applied learning, with exercises designed to help participants implement machine learning models from start to finish. 

Key Takeaways 

• Gain familiarity with the machine learning pipeline, from data preparation to model deployment 

• Learn to apply popular algorithms such as regression, classification, clustering, and ensemble methods 

• Practice model evaluation, tuning, and interpretation 

• Build and compare models using scikit-learn and Python 

• Address issues like overfitting, bias, and model selection through hands-on labs 

• Translate business problems into machine learning solutions 

Prerequisites 

• Basic knowledge of Python programming 

• Understanding of high school-level statistics and algebra 

• No prior machine learning experience required 

Module 1: Introduction to Machine Learning and the Model Workflow 

• What is Machine Learning? Definitions and Real-World Use Cases 

• Types of Learning: Supervised, Unsupervised, and Reinforcement (focus on the first two) 

• Overview of the ML Workflow: Data, Features, Model, Evaluation 

• Tools Overview: Python, Pandas, scikit-learn 

• Hands-On Exercise: Load and explore a dataset, perform basic preprocessing using Pandas 

Module 2: Supervised Learning – Regression and Classification 

• Linear Regression: Concept, Assumptions, and Use Cases 

• Logistic Regression: Binary Classification 

• Decision Trees and Random Forests 

• Evaluation Metrics: MAE, RMSE, Accuracy, Precision, Recall, Confusion Matrix 

• Cross-Validation and Train-Test Splitting 

• Hands-On Exercise: Build and compare regression and classification models using scikit-learn 

Module 3: Unsupervised Learning – Clustering and Dimensionality Reduction 

• K-Means Clustering: Concepts, Choosing K, Practical Applications 

• Hierarchical Clustering Overview 

• Dimensionality Reduction: PCA (Principal Component Analysis) 

• Feature Scaling and Data Normalization 

• Visualizing Clusters and Components 

• Hands-On Exercise: Apply K-Means and PCA to a customer segmentation dataset 

Module 4: Model Optimization, Interpretation, and Capstone 

• Feature Selection and Feature Engineering 

• Hyperparameter Tuning with GridSearchCV 

• Avoiding Overfitting: Regularization and Ensemble Methods (e.g., Gradient Boosting) 

• Model Interpretation: Feature Importance and Explainability 

• Ethics and Bias in Machine Learning