Introduction to Machine Learning for Data Science Course Course

Module 1: Introduction & Environment Setup

⏳ 30 minutes

• Installing Python, Jupyter Notebook, and key libraries (scikit-learn, pandas, matplotlib)

• Overview of the ML workflow and dataset exploration

Module 2: Data Preprocessing & Feature Engineering

⏳ 1 hour

• Handling missing data, outliers, and normalization/standardization

• Creating new features, encoding categoricals, and dimensionality reduction (PCA)

Module 3: Supervised Learning – Regression

⏳ 1 hour

• Implementing linear and polynomial regression with scikit-learn

• Assessing model fit, regularization techniques (Ridge, Lasso), and bias-variance trade-off

Module 4: Supervised Learning – Classification

⏳ 1 hour

• Training logistic regression, k-nearest neighbors, and decision tree classifiers

• Hyperparameter tuning with grid search and evaluating with confusion matrices

Module 5: Unsupervised Learning

⏳ 45 minutes

• Applying k-means clustering and hierarchical clustering for segmentation

• Using Gaussian mixture models and silhouette scores for cluster validation

Module 6: Ensemble Methods & Advanced Models

⏳ 1 hour

• Boosting (AdaBoost, Gradient Boosting) and bagging (Random Forest) techniques

• Understanding feature importance and improving model robustness

Module 7: Model Evaluation & Validation

⏳ 45 minutes

• Cross-validation strategies, learning curves, and ROC/AUC analysis

• Addressing class imbalance with resampling and metric selection

Module 8: Deployment & Best Practices

⏳ 30 minutes

• Building a simple prediction pipeline and saving models with joblib

• Key considerations for production: latency, monitoring, and data drift

Get certificate