Applied Machine Learning in Python Course Syllabus

This intermediate-level course provides a hands-on introduction to machine learning in Python using scikit-learn. Over approximately 4 weeks with 6–8 hours of study per week, learners will gain practical experience building, tuning, and evaluating predictive models. The curriculum emphasizes real-world workflows, from data preparation to model optimization, using widely adopted tools and techniques. Each module combines conceptual understanding with coding exercises on real datasets to reinforce core machine learning skills.

Module 1: Fundamentals of Machine Learning

Estimated time: 6 hours

• Machine learning lifecycle and core concepts
• Supervised vs. unsupervised learning
• Introduction to scikit-learn library
• Building K-nearest neighbors models
• Implementing linear regression with scikit-learn

Module 2: Decision Trees & Random Forests

Estimated time: 7 hours

• Decision trees for classification and regression
• Ensemble learning with random forests
• Feature importance interpretation
• Model evaluation using cross-validation

Module 3: Clustering & Feature Engineering

Estimated time: 7 hours

• K-means clustering for unlabeled data
• Feature scaling and dimensionality considerations
• Feature engineering techniques
• Improving model performance through preprocessing

Module 4: Ensemble Methods & Model Optimization

Estimated time: 7 hours

• Bagging and boosting methods
• Gradient boosting implementation
• Hyperparameter tuning with grid search
• Cross-validation for model improvement
• Overfitting prevention strategies

Module 5: Applied Machine Learning Workflows

Estimated time: 6 hours

• End-to-end ML pipeline construction
• Data preprocessing and train-test split
• Model selection and performance comparison
• Practical workflows for real-world projects

Module 6: Final Project

Estimated time: 8 hours

• Build a complete predictive model using real data
• Apply feature engineering and model tuning
• Evaluate and interpret results using scikit-learn

Prerequisites

• Familiarity with Python programming
• Experience with Pandas and NumPy libraries
• Basic understanding of data visualization and exploratory analysis

What You'll Be Able to Do After

• Build and evaluate supervised and unsupervised machine learning models
• Apply ensemble methods like random forests and gradient boosting
• Perform feature engineering and model validation using scikit-learn
• Tune hyperparameters and prevent overfitting in predictive models
• Implement end-to-end machine learning workflows on real datasets