Machine Learning
Every core machine learning algorithm, taught with code and real projects — regression, classification, ensembles, clustering, time series, recommendations, pipelines and deployment.
https://github.com/waleedkhokar/python-ai-engineering-journey/tree/main/03%20Machine-Learning — so you always land in the right place, never on a guessed sub-folder.What You Will Learn
All Classes
Every class in this course, in order — with exactly what each class teaches. Notes, code and interview prep for each class live in the course folder on GitHub.
Setup & Environment
Scikit-Learn, XGBoost, LightGBM, CatBoost, Jupyter, requirements.txt.
ML Fundamentals
AI vs ML vs DL, supervised and unsupervised learning, bias vs variance, overfitting, the ML workflow.
Data Preprocessing
Missing values, encoding (one-hot, label, target), scaling (standard, min-max, robust), train/test split.
Regression
Linear, polynomial, Ridge, Lasso, ElasticNet — and the metrics MAE, MSE, RMSE, R².
Classification
Logistic regression, KNN, Naive Bayes, SVM, decision trees — accuracy, precision, recall, F1, ROC-AUC.
Ensemble Methods
Random Forest, XGBoost, LightGBM, CatBoost, AdaBoost, stacking, voting.
Clustering
K-Means, hierarchical clustering, DBSCAN, GMM, silhouette score, elbow method.
Dimensionality Reduction
PCA, t-SNE, UMAP, LDA, explained variance.
Model Evaluation
Cross-validation, K-fold, stratified folds, confusion matrix, ROC curves, learning curves.
Hyperparameter Tuning
GridSearchCV, RandomizedSearchCV, Optuna, Bayesian optimization.
Feature Selection
Filter methods (correlation, chi-square), wrapper methods (RFE), embedded methods (Lasso, tree importance).
Time Series
ARIMA, SARIMA, Prophet, LSTM, forecasting, MAE / RMSE / MAPE.
Recommendation Systems
Content-based filtering, collaborative filtering, matrix factorization, the cold-start problem.
NLP Basics
Text preprocessing, Bag of Words, TF-IDF, word embeddings, sentiment analysis.
ML Pipelines
scikit-learn Pipeline, ColumnTransformer, custom transformers, end-to-end workflows.
MLflow & Experiment Tracking
Track experiments, parameters, metrics and artifacts — plus the model registry.
Model Deployment
joblib and pickle, FastAPI, Docker, Streamlit, cloud deployment.
Projects in this course
Build them all — each project lives in the course folder on GitHub.
Interview Preparation
Every class in this course includes interview preparation. Open the course folder on GitHub for the full material.
Common Interview Questions
The questions interviewers actually ask on this course's topics, with clear, structured answers.
Open Interview PrepCoding Challenges
Hands-on problems for each class, so you can prove the skill, not just describe it.
Open Interview PrepCommon Mistakes to Avoid
The errors beginners make with these topics — and how professionals avoid them.
Open Interview PrepBest Practices
How working engineers write this code in production: clean, tested and maintainable.
Open Interview Prep