Machine Learning for Beginners
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Master Machine Learning
from the
Beginning.
A structured 10-module pathway covering ML foundations, Python, preprocessing, EDA, regression, classification, tree ensembles, unsupervised and semi-supervised learning, reinforcement learning, deployment, and MLOps.
Skills you'll gain from this course
Designed for complete beginners — built to take you from Python and data preparation to model training, evaluation, deployment, and monitoring.
Understand the four core types of machine learning and the complete ML workflow
Use NumPy, Pandas, Matplotlib, Seaborn, and the Scikit-learn API
Clean data, scale variables, encode categories, engineer features, and prevent leakage
Perform univariate, bivariate, multivariate, time-series, and outlier analysis
Build and evaluate regression, classification, tree, and ensemble models
Apply clustering, dimensionality reduction, and association-rule methods
Use self-training and label propagation when labeled data is limited
Understand Q-Learning, APIs, Streamlit, model versioning, and monitoring
10 modules · Theory · Video · Simulator · Notes · Python · FAQs · MCQs
Every module includes a video lesson, a 16:9 theory presentation, expanded textbook notes, executable Python examples, an interactive simulator, FAQs, and aligned MCQs.
Browse all 10 modules
Click any module to jump straight in.
Ready to complete the full beginner ML pathway?
Learn Python, modeling, evaluation, deployment, and monitoring across all 10 modules — completely free.
