Machine Learning (Supervised, Unsupervised, Reinforcement) — using Python & Scikit-learn

Categories: AI/ML, CSE

About Course

The Machine Learning (Supervised, Unsupervised, Reinforcement) course provides a complete foundation for understanding, building, and deploying intelligent systems using Python and Scikit-learn.
You’ll start from the basics of data preprocessing and model training and progress to advanced ML topics like reinforcement learning, model evaluation, and tuning.

The course emphasizes hands-on learning — every module includes coding exercises, datasets, and real-world projects. By the end, you’ll be able to develop predictive models, optimize algorithms, and interpret results confidently — skills that form the backbone of modern AI and Data Science careers.

Grasp the fundamentals of Machine Learning and AI workflows.
Preprocess, clean, and visualize datasets using Python libraries.
Build supervised models (Linear Regression, Decision Trees, SVM).
Implement unsupervised learning (K-Means, PCA, Clustering).
Understand reinforcement learning and agent-environment dynamics.
Evaluate models using metrics like precision, recall, and F1-score.
Perform hyperparameter tuning and model optimization.
Apply ML to real-world projects using Scikit-learn.

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Machine Learning (Supervised, Unsupervised, Reinforcement) — using Python & Scikit-learn

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