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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 ApplicationsVideo · 20:00
FREE
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The Machine Learning PipelineVideo · 25:00
FREE
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Data Preprocessing — Encoding & ScalingVideo · 30:00
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Train-Test Split & Cross-ValidationVideo · 25:00
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Overfitting vs UnderfittingVideo · 22:00
02Supervised Learning Algorithms
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Linear Regression — Theory & MathVideo · 35:00
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Support Vector Machines (SVM)Video · 32:00
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K-Nearest Neighbors (KNN)Video · 22:00
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Naive Bayes — Probabilistic ClassificationVideo · 20:00
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Gradient Boosting — XGBoost & LightGBMVideo · 38:00
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Hyperparameter Tuning — GridSearch & RandomSearchVideo · 28:00
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Project — House Price Prediction ModelProject · 90:00
03Model Evaluation Mastery
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Classification Metrics Deep DiveVideo · 30:00
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Dealing with Imbalanced DatasetsVideo · 25:00
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Regression Metrics & Residual AnalysisVideo · 22:00
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Project — ML Model Comparison ReportProject · 60:00
04Unsupervised Learning
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Clustering Algorithms — K-Means, DBSCAN, HierarchicalVideo · 35:00
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PCA — Dimensionality ReductionVideo · 30:00
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Anomaly DetectionVideo · 22:00
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Project — Customer Segmentation with K-MeansProject · 75:00
05Deployment & MLOps Basics
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Saving & Loading Models with Pickle & JoblibVideo · 18:00
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Building an ML API with FastAPIVideo · 35:00
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Model Monitoring & Drift DetectionVideo · 22:00
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Capstone — Full ML Project from Data to APIProject · 120:00