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📚 What you'll learn
✓ML types: supervised, unsupervised, reinforcement
✓Linear & logistic regression with math
✓SVM, KNN, Naive Bayes, Random Forest
✓XGBoost and gradient boosting mastery
✓Model evaluation: precision, recall, F1, AUC
✓Hyperparameter tuning with GridSearchCV
✓Clustering: K-Means, DBSCAN, Hierarchical
✓Deploy ML models as FastAPI REST APIs
Course Curriculum
01What is Machine Learning?
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ML Overview — Types and ApplicationsLesson
FREE
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The Machine Learning PipelineLesson
FREE
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Data Preprocessing — Encoding & ScalingLesson
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Train-Test Split & Cross-ValidationLesson
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Overfitting vs UnderfittingLesson
02Supervised Learning Algorithms
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Linear Regression — Theory & MathLesson
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Support Vector Machines (SVM)Lesson
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K-Nearest Neighbors (KNN)Lesson
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Naive Bayes — Probabilistic ClassificationLesson
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Gradient Boosting — XGBoost & LightGBMLesson
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Hyperparameter Tuning — GridSearch & RandomSearchLesson
🛠️
Project — House Price Prediction ModelProject
03Model Evaluation Mastery
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Classification Metrics Deep DiveLesson
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Dealing with Imbalanced DatasetsLesson
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Regression Metrics & Residual AnalysisLesson
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Project — ML Model Comparison ReportProject
04Unsupervised Learning
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Clustering Algorithms — K-Means, DBSCAN, HierarchicalLesson
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PCA — Dimensionality ReductionLesson
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Anomaly DetectionLesson
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Project — Customer Segmentation with K-MeansProject
05Deployment & MLOps Basics
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Saving & Loading Models with Pickle & JoblibLesson
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Building an ML API with FastAPILesson
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Model Monitoring & Drift DetectionLesson
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Capstone — Full ML Project from Data to APIProject