It's great that you've found a passion for Machine Learning (ML) at such an early stage in your career. Your interest in the underlying math is a fantastic starting point, as it's a critical component of understanding and excelling in the field. Here's a structured approach to help you move from an explorer to a professional in ML.
Don't just watch videos; focus on understanding the core concepts.
Deepen Your Math Knowledge: Since you love math, this will be an enjoyable part of your journey. Linear algebra, calculus, and probability and statistics are the pillars of ML. Courses from institutions like MIT and Stanford are available online and will provide a much deeper understanding than many quick tutorials. For instance, understanding the dot product from linear algebra is key to comprehending how neural networks process data.
Master the Fundamentals of ML: Andrew Ng's course on Coursera is an excellent choice for a conceptual introduction. Follow it up with courses that go deeper into specific areas like deep learning and reinforcement learning. A strong grasp of concepts like gradient descent, backpropagation, and bias-variance tradeoff is non-negotiable.
Theory without practice is not enough in a field like ML.
Choose a Programming Language: Python is the industry standard for ML. Get comfortable with libraries like NumPy for numerical operations, Pandas for data manipulation, and Matplotlib/Seaborn for data visualization. These are the tools you'll use every day.
Work on Projects, Not Just Tutorials: Instead of just following tutorials, try to build your own projects from scratch. Kaggle is an excellent platform for this. Participate in competitions, work on their datasets, and learn from the code shared by others. A simple project could be building a model to classify images of cats and dogs.
Contribute to Your Own Portfolio: Start a GitHub repository and upload all your projects, even the small ones. This will serve as your public portfolio, a non-negotiable asset that recruiters and hiring managers will look at. Your code will speak louder than any resume.
The ML field is incredibly dynamic, with new research and tools emerging constantly.
Join the Community: Platforms like Stack Overflow, Reddit (r/Machine Learning, r/learn machine learning), and dedicated Slack/Discord channels are great for asking questions and learning from others. Engage in discussions and help out where you can.
Read Research Papers: As you progress, start reading and trying to implement ideas from research papers. This is how you'll stay at the cutting edge. Websites like arXiv and Google Scholar are your best friends here. You don't have to understand everything at first, but get used to the language.
Attend Hackathons and Meetups: If there are local tech meetups or hackathons, participate in them. This is a great way to network with professionals and other students, learn from their experience, and work on collaborative projects.
By following these steps, you will not only acquire the necessary skills but also build a compelling professional profile that will set you up for success in the field of Machine Learning.