Introduction to Machine Learning for Data Science Course Syllabus

Overview (80-120 words) describing structure and time commitment.

Module 1: Introduction & Environment Setup

Estimated time: 0.5 hours

• Installing Python, Jupyter Notebook, and key libraries (scikit-learn, pandas, matplotlib)
• Overview of the machine learning workflow
• Dataset exploration basics
• Setting up a reproducible coding environment

Module 2: Data Preprocessing & Feature Engineering

Estimated time: 1 hour

• Handling missing values and outliers
• Normalization and standardization techniques
• Encoding categorical variables
• Feature creation and selection
• Dimensionality reduction with PCA

Module 3: Supervised Learning – Regression

Estimated time: 1 hour

• Implementing linear and polynomial regression
• Evaluating model fit using MSE and R-squared
• Understanding bias-variance trade-off
• Regularization with Ridge and Lasso regression

Module 4: Supervised Learning – Classification

Estimated time: 1 hour

• Training logistic regression and k-nearest neighbors classifiers
• Building and interpreting decision trees
• Using confusion matrices for evaluation
• Hyperparameter tuning with grid search

Module 5: Unsupervised Learning

Estimated time: 0.75 hours

• Applying k-means clustering for data segmentation
• Exploring hierarchical clustering methods
• Using Gaussian mixture models
• Evaluating clusters with silhouette scores

Module 6: Ensemble Methods & Advanced Models

Estimated time: 1 hour

• Implementing Random Forest with bagging
• Applying AdaBoost and Gradient Boosting
• Analyzing feature importance
• Improving model robustness through ensembling

Module 7: Model Evaluation & Validation

Estimated time: 0.75 hours

• Using cross-validation strategies
• Interpreting learning curves
• ROC curve and AUC analysis
• Handling class imbalance with resampling
• Selecting appropriate performance metrics

Module 8: Deployment & Best Practices

Estimated time: 0.5 hours

• Building a simple prediction pipeline
• Saving and loading models using joblib
• Understanding production concerns: latency and monitoring
• Recognizing data drift in deployed models

Prerequisites

• Basic understanding of Python programming
• Familiarity with fundamental math concepts (algebra, statistics)
• Experience with Jupyter Notebooks is helpful but not required

What You'll Be Able to Do After

• Build and train supervised and unsupervised machine learning models
• Preprocess real-world datasets and engineer meaningful features
• Evaluate models using appropriate metrics and validation techniques
• Apply ensemble methods to improve predictive performance
• Deploy models into simple production-ready pipelines