Mastering Machine Learning from Scratch

From Linear & Logistic Regression to Decision Trees, Ensembles, SVM and Clustering — with hands-on projects

What you'll learn
- Understand the core concepts of Machine Learning and how models learn from data.
- Implement popular ML algorithms like Linear Regression, Decision Trees, and Random Forests from scratch and with libraries.
- Evaluate, tune, and improve models using cross-validation, regularization, and performance metrics.
- Apply ML techniques on real-world datasets and build end-to-end projects for practical experience.

Requirements
- Basic understanding of Python programming and mathematics (algebra, statistics).
- No prior Machine Learning knowledge required — everything will be taught step by step.

Description
Mastering Machine Learning from Scratchis a complete step-by-step course designed to take you from beginner to confident practitioner. This course is structured in a way that builds strong foundations before moving into advanced topics, ensuring you not only learn algorithms but also understand the “why” behind them.

We start with anIntroduction to Machine Learning, where you’ll understand the types of ML and real-world applications. From there, we move intoSupervised Learning (Regression)covering Linear Regression in detail — from theory, gradient descent, and implementation, to advanced concepts likebias-variance tradeoff, regularization (L1 & L2), cross-validation, polynomial regression, and model evaluation.

Next, you’ll exploreClassification algorithmsincluding Logistic Regression and Decision Trees, learning both the theory and coding implementations. Building on this, we dive intoEnsemble Learningtechniques likeBagging, Boosting, Stacking, Random Forest, and XGBoost, which are widely used in industry today.

The course then introducesNon-Linear Algorithmssuch asK-Nearest Neighbors (KNN)andSupport Vector Machines (SVM), followed byUnsupervised Learning, where you’ll masterK-Means, Hierarchical Clustering, and PCAalong with evaluation techniques like theElbow Method and Silhouette Score.

Each section comes withquizzes to test your knowledge, and the course concludes withcapstone projects:

By the end of this course, you will have hands-on experience in implementing end-to-end ML workflows — from data preprocessing to model building and evaluation. Whether you’re preparing for acareer in data science, looking to strengthen yourML fundamentals, or working onreal-world projects, this course will give you the right balance of theory, coding, and practical application.

Who this course is for:
- Beginners and professionals who want to learn Machine Learning from scratch and apply it in real-world scenarios.
- Students preparing for careers in Data Science, AI, or Analytics who want hands-on ML skills.
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