fast.ai

To start with Machine Learning: 1. Learn Python 2. Practice using Google Colab Take these 2 free courses: • Introduction to Python Programming (Udacity) • Machine Learning Crash Course (Google) If you need a bit more time before diving deeper, finish the following Kaggle tutorials: • Intro to Machine Learning • Intermediate Machine Learning At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle. If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed. From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit. The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them: • MIT 6.S191 Introduction to Deep Learning • DS-GA 1008 Deep Learning • UC Berkeley Full Stack Deep Learning • UC Berkeley CS 182 Deep Learning • Cornell Tech CS 5787 Applied Machine Learning Many different books will help you. The attached image will give you an idea of my favorite ones. Finally, keep these three ideas in mind: 1. Start by working on solved problems so you can find help whenever you get stuck. 2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice. 3. Find a community here on 𝕏 and share your work. Ask questions, and help others. During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right. Here are the good news: Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in the space. Focus on finding your path, and Write. More. Code. That's how you win.