There are numerous reasons why mathematics for Machine Learning is significant, and I will be sharing a few of the important pointers below:
- Choosing the best algorithm requires taking into account accuracy, training time, model complexity, number of parameters, and number of features.
- Choosing parameter values and validation methods.
- Understanding the Bias-Variance tradeoff allows you to identify underfitting and overfitting issues that normally occur while executing the program.
- Determining the correct confidence interval and uncertainty.
What is the proper way to learn Maths For Data Science And Machine Learning?
Although there are plenty of valuable resources available on the internet which explains concepts like matrix decompositions vector calculus, linear algebra analytic geometry matrix, maths behind the principal component analysis, and support vector machines. Not all resources are a one-stop solution for your understanding. Hence, I have collated a list of books, websites, and youtube channels that can help you better your theoretical concept in the field of artificial intelligence.
- Mathematics for Machine Learning by Marc Peter Deisenroth is the book that can help you to start your mathematical journey. Practical applications of the algorithms and the maths behind them have been clearly explained. All the concepts of mathematics have been properly explained- You can refer to the online pdf here -https://mml-book.github.io/book/mml-book.pdf
- Multivariate Calculus by Imperial College London – Imperial College London has basically come up with a YouTube series that covers the important concepts of multivariate calculus and its application in various ml algorithms. Although the entire course is in collaboration with Coursera, Imperial College London has made it available for free for all the inquisitive learners.
- Khan Academy’s courses on Linear Algebra, Probability & Statistics, Multivariable Calculus, and Optimization– A very comprehensive and free resource available for all the learners to further their knowledge in complex concepts like linear algebra analytic geometry matrix.
- All of statistics: A Concise Course in Statistical Inference by Larry Wasserman is supposedly another exhaustive resource that contains a detailed explanation of important concepts like
- Udacity’s Introduction to Statistics– is another free resource through which you can get an initial level of understanding in the field of statistics that is needed for data science.