Overview
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.
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.
Basic understanding of Python programming and mathematics (algebra, statistics)., No prior Machine Learning knowledge required — everything will be taught step by step.
Mastering Machine Learning from Scratch is 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 an Introduction to Machine Learning, where you’ll understand the types of ML and real-world applications. From there, we move into Supervised Learning (Regression) covering Linear Regression in detail — from theory, gradient descent, and implementation, to advanced concepts like bias-variance tradeoff, regularization (L1 & L2), cross-validation, polynomial regression, and model evaluation.
Next, you’ll explore Classification algorithms including Logistic Regression and Decision Trees, learning both the theory and coding implementations. Building on this, we dive into Ensemble Learning techniques like Bagging, Boosting, Stacking, Random Forest, and XGBoost, which are widely used in industry today.
The course then introduces Non-Linear Algorithms such as K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), followed by Unsupervised Learning, where you’ll master K-Means, Hierarchical Clustering, and PCA along with evaluation techniques like the Elbow Method and Silhouette Score.
Each section comes with quizzes to test your knowledge, and the course concludes with capstone 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 a career in data science, looking to strengthen your ML fundamentals, or working on real-world projects, this course will give you the right balance of theory, coding, and practical application.
Deeshu Pandit
I am Deeshu Kumar Pandit, a Data Scientist with hands-on experience in Machine Learning, AI, and FinTech solutions. I have worked on real-world projects in credit risk modeling, time-series forecasting, NLP, and large-scale ETL pipelines using Python, SQL, and AWS. Alongside my professional work, I teach full-stack Data Science through my own platform and YouTube channel, where I simplify complex ML concepts into beginner-friendly lessons. My goal is to help learners not just understand theory but also apply Machine Learning to solve real-world problems.