MACHINE LEARNING
Machine Learning (ML) is a subset of artificial intelligence that focuses on developing systems that can learn and improve from experience without being explicitly programmed.
Types of Machine Learning
1. Supervised Learning
- Classification
- Regression
- Support Vector Machines
- Neural Networks
- Decision Trees
- Random Forests
2. Unsupervised Learning
- Clustering
- Dimensionality Reduction
- Anomaly Detection
- Association Rule Learning
- Principal Component Analysis (PCA)
3. Reinforcement Learning
- Q-Learning
- Deep Q Networks
- Policy Gradient Methods
- Actor-Critic Methods
- Monte Carlo Methods
Way to Be Followed
- HarvardX: Introduction to Probability - EdX
- UTAustinX: Linear Algebra - Foundations to Frontiers - EdX
- Matrix Algebra for Engineers - Coursera
- Machine Learning Specialization - Coursera
- Deep Learning Specialization - Coursera
Resources
- Google Machine Learning Crash Course
- Stanford CS229: Machine Learning
- Dataquest Courses
- Scikit-learn Courses
- Fast.ai
- TensorFlow Documentation
- PyTorch Tutorials
Documentation
- An Ideal Roadmap for Beginners
- Machine Learning and Data Analysis Guide
- Machine Learning with PyTorch
- The Complete Machine Learning Course with Python
Data Scientist in Python Lectures by Dataquest
Python Introduction
Useful Links
- Kaggle: Platform for data science competitions and datasets
- Google Colab: Free Jupyter notebook environment with GPU support
- Papers with Code: Machine learning papers with implementation code
- Hugging Face: The AI community building the future
- Scikit-learn: Machine Learning in Python
- OpenAI Gym: Toolkit for developing reinforcement learning algorithms
- How I’d learn ML in 2024 (if I could start over) - Boris Meinardus
- Introduction To Machine Learning - Simplilearn
- Build Your First Machine Learning Project - Dataquest
- Build Your First Machine Learning Model in Python - Data Professor