Machine Learning with Python Course Syllabus

Overview: This course provides a practical introduction to machine learning using Python, designed for professionals seeking to build foundational skills. Over six modules, learners gain hands-on experience with real-world datasets and tools like Scikit-learn. The course spans approximately six weeks with a commitment of 3–5 hours per week, combining theory, interactive labs, and a capstone project to solidify learning.

Module 1: Introduction to Machine Learning

Estimated time: 4 hours

• Understanding machine learning fundamentals
• Types of machine learning: supervised, unsupervised, and reinforcement learning
• Real-world applications of machine learning
• Setting up the Python environment for machine learning

Module 2: Supervised Learning

Estimated time: 5 hours

• Introduction to regression and classification
• Linear regression models
• Decision trees and random forests
• Support vector machines (SVM)

Module 3: Unsupervised Learning

Estimated time: 5 hours

• Clustering with k-means algorithm
• Hierarchical clustering techniques
• Dimensionality reduction using PCA

Module 4: Model Evaluation and Refinement

Estimated time: 5 hours

• Understanding overfitting and underfitting
• Performance metrics: accuracy, precision, recall, F1-score
• Cross-validation and train-test splits
• Hyperparameter tuning and model selection

Module 5: Building ML Models with Scikit-learn

Estimated time: 5 hours

• Implementing machine learning pipelines
• Training and testing models using Scikit-learn
• Model evaluation and refinement in practice

Module 6: Final Project

Estimated time: 6 hours

• Apply supervised and unsupervised learning techniques to real-world data
• Build, evaluate, and refine a predictive model
• Submit a comprehensive project report with findings and insights

Prerequisites

• Basic knowledge of Python programming
• Familiarity with fundamental statistics concepts
• Comfort with data manipulation and analysis concepts

What You'll Be Able to Do After

• Explain core machine learning principles and their applications
• Build and evaluate supervised and unsupervised learning models
• Use Scikit-learn to implement real-world machine learning solutions
• Evaluate model performance and apply refinement techniques
• Demonstrate skills through a capstone project using real data