Master machine learning algorithms and techniques. Learn how to build intelligent systems that learn from data.
Build intelligent systems that learn and make predictions
Create prediction and classification models using linear regression, logistic regression, decision trees, and neural networks with high accuracy
Apply ML to business, healthcare, finance, and research challenges. Grasp the mathematics behind ML without getting overwhelmed
Work with scikit-learn, TensorFlow, and industry-standard tools. Build portfolio projects and skills for ML engineering roles
Basic Python knowledge required (variables, loops, functions)
Algebra and basic statistics (we'll explain concepts as we go)
Python installed with Jupyter Notebook or Google Colab
Foundation concepts and essential algorithms
Understand the fundamentals of ML. Learn about supervised, unsupervised, and reinforcement learning.
Coming SoonMaster the most fundamental ML algorithm. Learn how to predict continuous values from data.
Coming SoonLearn classification algorithms. Understand how to predict categories and make binary decisions.
Coming SoonBuild tree-based models for classification and regression. Understand feature importance and interpretability.
Coming SoonPowerful algorithms and ensemble methods
Master ensemble learning. Learn how combining multiple models creates more powerful predictions.
Coming SoonLearn powerful classification techniques. Understand kernels and how to find optimal decision boundaries.
Coming SoonMaster XGBoost and LightGBM. Learn the algorithms that win Kaggle competitions and power real-world ML.
Coming SoonBuild real-world machine learning applications
Build a complete regression project. Learn data cleaning, feature engineering, and model deployment.
Coming SoonBuild a classification system. Predict which customers are likely to leave and why.
Coming SoonBuild a product recommendation engine. Learn collaborative filtering and content-based recommendations.
Coming SoonExplore more courses to expand your AI and programming skills