All Courses & Projects

Introduction to Machine Learning with Python

Machine learning is now used everywhere. Want to learn it yourself? This course is an introduction to the world of Machine learning for you to learn basic concepts, work with Scikit-learn – the most popular library for ML and build your first Machine Learning project. This course is intended for students with a basic knowledge of Python, Pandas, and Numpy.

Linear Regression with Python

Linear Regression is a crucial concept in predictive analytics. It is widely used by data scientists, data analytics, and statisticians as it is easy to build and interpret but powerful enough for many tasks.

Classification with Python

Master the core classification algorithms that power modern machine learning. Explore how models like k-NN, logistic regression, decision trees, and random forests make predictions, evaluate their accuracy, and understand when to use each. Build the skills to compare models and choose the best one for your data.

Cluster Analysis with Python

Gain a solid understanding of cluster analysis, a key unsupervised learning technique for uncovering patterns in unlabeled data. Explore the essentials of K-Means, Hierarchical Clustering, DBSCAN, and GMMs, and get hands-on experience with real datasets to build confidence in applying clustering to real-world problems.

Data Preprocessing and Feature Engineering with Python

Learn practical techniques to clean, transform, and engineer data for machine learning using Python. This course covers essential preprocessing steps, feature creation, and hands-on challenges to prepare data for modeling.

Evaluation Metrics in Machine Learning with Python

A comprehensive course for intermediate learners to master the key evaluation metrics used in machine learning, covering both supervised and unsupervised tasks. The course progresses from foundational concepts to practical Python implementations, interpretation of results, and a comparative overview of metric types and their trade-offs.

Loss Functions in Machine Learning

A comprehensive theoretical exploration of loss functions in machine learning, covering mathematical foundations, geometric intuition, and practical implications for model optimization and evaluation.

Bio-Inspired Algorithms with Python

Explore the foundations and practical applications of bio-inspired algorithms in Python. This course covers evolutionary computation, swarm intelligence, and other nature-inspired optimization techniques, using only standard Python and permitted scientific libraries.

Feature Encoding Methods in Python

Master advanced categorical feature encoding methods in Python, including Weight-of-Evidence, Leave-one-out, Helmert, and high-cardinality encodings. Learn to avoid encoding leakage and apply robust techniques for real-world data science projects.

Feature Selection and Regularization Techniques in Python

A comprehensive intermediate course on regularization and feature selection in Python using scikit-learn, numpy, pandas, matplotlib, and seaborn. Learn to combat overfitting, apply L1/L2/Elastic Net regularization, select features using various strategies, and build robust pipelines for real-world data science tasks.

Optimization Methods in Machine Learning in Python

A rigorous, intuition-driven exploration of the mathematical foundations and optimization algorithms that power modern machine learning. This course blends theory, geometric intuition, and Python-based visualizations to build a deep understanding of how optimization works in ML.

Advanced Tree-Based Models with Python

Master the most powerful modern tree-based ensemble methods—CatBoost, XGBoost, and LightGBM. Learn their unique innovations, practical tuning, and how to leverage them for high-performance machine learning tasks.

Hyperparameter Tuning Basics with Python

A comprehensive intermediate course guiding learners through the essentials of hyperparameter tuning in machine learning, from foundational theory to advanced automated techniques, with hands-on scikit-learn examples.

Bayesian Statistics and Probabilistic Modeling

A theory-focused course exploring Bayesian statistics as a framework for reasoning under uncertainty, emphasizing mathematical intuition, conceptual understanding, and probabilistic modeling.

Data Privacy and Differential Privacy Fundamentals

An intermediate, theory-first course exploring the essentials of data privacy, classical anonymization, and the foundations and mechanisms of Differential Privacy (DP), with practical Python demonstrations and conceptual quizzes.

Technologies

Topic

Level

Type of lesson

Career tracks

Supervised Learning Foundations

Courses & Projects

Career tracks

Supervised Learning Foundations

track

Supervised Learning Foundations

Courses & Projects

Technologies