Machine Learning (ML) Roadmap
Mathematical Foundations (Must know before ML)

1. Algebra
2. Trigonometry
3. Basic Geometry
4. Precalculus (including complex numbers)
5. Linear Algebra
6. Single-Variable Calculus
7. Multivariable Calculus
8. Probability & Statistics
9. Optimization

Programming & Computational Skills

1. Python (NumPy, Pandas, Matplotlib, SciPy)
2. Data Structures & Algorithms (optional)
3. Object-Oriented Programming (OOP)
4. Git/GitHub

Core ML Concepts

1. Supervised & Unsupervised Learning
2. Regression Models (Linear, Logistic)
3. Classification Models (Decision Trees, SVMs, k-NN)
4. Clustering (K-means, DBSCAN)
5. Neural Networks (MLP, Backpropagation)
6. Deep Learning (CNNs, RNNs, Transformers)
7. Feature Engineering & Data Preprocessing
8. Model Evaluation & Metrics

Advanced Topics

1. Optimization for Deep Learning
2. Probabilistic Graphical Models
3. Reinforcement Learning
4. Generative Models (GANs, VAEs)
5. AI Ethics & Bias Mitigation
6. Scalable ML (Big Data, Distributed Training)
7. Mathematical ML (Kernel Methods, Info Theory)

Practical Applications

1. Computer Vision
2. Natural Language Processing (NLP)
3. Robotics & Autonomous Systems

The mathematical preliminaries can be omitted if you are looking to just learn practical ML.

"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
   - This book is widely acclaimed for its practical approach, offering hands-on tutorials and real-world examples.

#Books #ML #AI

And most of the topics mentioned are discussed on this book at basic-intermediate level.

This is a detailed introduction to the field of pattern recognition and machine learning. At the end of each chapter, there are exercises designed to better explain each concept to the reader.

This one contains hard core math. It is full of abuse of linear algebra and calc 3.

It teaches you how the algorithms work under the hood.

Most of the math you will ever need are contanied in these books whether you are into physics, engineering or ML.

Linear algebra done right by Axler

Another alternative to the books above. It is short and concise but has a steeper learning curve.

🔰 Complete Machine Learning and Data Science 2021

⏱43 Hours 📦 371 Lessons

Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!