Complete Machine Learning Course with Projects

About this path

This learning path takes you from the foundations of machine learning through supervised and unsupervised learning, model tuning, and ensemble methods. You'll then explore natural language processing and deep learning architectures, including artificial neural networks, convolutional neural networks, recurrent neural networks, and Transformers. Each lesson includes clear explanations, coding examples, and practical projects using Python and Scikit-learn. The path is designed for beginners with basic Python and statistics knowledge, and it builds up to interview-ready concepts.

What you will learn

• Understand the complete workflow of a real-world machine learning project
• Implement supervised learning models for regression and classification
• Apply model tuning, ensemble methods, and unsupervised learning techniques
• Build NLP models using bag-of-words and TF-IDF
• Implement and understand artificial neural networks, CNNs, RNNs, and Transformers

Who this is for

Beginners who want a structured free introduction before deeper practice.

Curriculum