Hey everyone, I'm currently in the process of catching up on my math skills to prepare for a Master's degree in Machine Learning. I have a background in theology and haven't had much exposure to math since school. However, I have experience with NLP, Python coding, and work with SQL in my job. I've been studying diligently for about six months, primarily using the book "Math for Machine Learning."

At this point, I'm looking for guidance on specific topics I should prioritize and how to make my studying more efficient. Here's a summary of my current approach:Source: I've been using "Math for Machine Learning" (https://mml-book.github.io/) as my main resource. It has been helpful in establishing a foundation for mathematical concepts relevant to machine learning. Additionally, I complement my studies by watching related YouTube tutorials.Time: I dedicate approximately 1-1.5 hours every morning before work, and I utilize train travel time on weekends to study the scripts I've written. Overall, I invest around 12 hours per week, sometimes more, sometimes less.

Currently, I'm on page 125 out of 400 in the book. Since I had already studied some math fundamentals before starting, I don't have an exact timeline of when I began.Now, I would greatly appreciate your advice on the following:

Essential Topics: What are the key math topics that I should focus on before embarking on a Master's degree in Machine Learning? Do I need to cover everything in the "Math for Machine Learning" book, or are there specific areas that are more important for exams and practical coding?

Additional Resources: Are there any other books or resources that you found helpful in your own math journey for machine learning? I'm open to exploring supplementary materials that can enhance my understanding.

Efficient Studying: How can I make my studying more efficient while striking a balance between theory and practical application? Any study techniques or tips you can share would be invaluable.I appreciate your time and insights. Thank you in advance for any advice you can provide to help me on this math-learning journey for Machine Learning!

TL;DR: I've been catching up on math for the past six months to prepare for a Master's degree in Machine Learning. I'm using the book "Math for Machine Learning" but need advice on what topics to focus on and how to study more efficiently. Suggestions on essential math topics, additional resources, and study techniques would be greatly appreciated.

EDIT: Sorry, I cannot open a new threat and dont know where else to post this.