Machine Learning
From fundamentals to cutting-edge techniques in machine learning.
What You'll Learn
- Neural networks and deep learning
- Convolutional and recurrent networks
- Transformers and large language models
- Training and optimization techniques
- Practical applications
Why This Matters
Machine learning is transforming how we solve problems across industries. Understanding these fundamentals is crucial for data scientists and ML engineers.
Structure & Chapters
This section contains 14 chapters organized in 5 subsections:
Foundations (3 chapters)
- Introduction to ML
- Linear Algebra Essentials
- Statistics & Probability
Supervised Learning (2 chapters)
- Regression
- Classification
Deep Learning (3 chapters)
- Neural Networks Basics
- Convolutional Networks
- Recurrent Networks
Transformers & LLMs (3 chapters)
- Attention Mechanism
- Transformers Explained
- Large Language Models
Training & Optimization (3 chapters)
- Training Fundamentals
- Optimization Techniques
- Practical Tips
Where to Start
Begin with Introduction to ML