The objective of this course is to give you a solid foundation needed to excel in all areas of computer science—specifically data science and machine learning. The issue is that most of the probability and statistics courses are too theory-oriented. They get tangled in the math without discussing the importance of applications. Applications are always given secondary importance.
In this course, we take a code-oriented approach. We apply all concepts through code. In fact, we skip over all the useless theory that isn’t relevant to computer science. Instead, we focus on the concepts that are more useful for data science, machine learning, and other areas of computer science. For instance, many probability courses skip over Bayesian inference. We will get to this immensely important concept rather quickly and give it due attention as it is widely thought of as the future of analysis!
This way, you get to learn the most important concepts in this subject in the shortest amount of time possible without having to deal with the details of the less relevant topics. Once you have developed an intuition of the important stuff, you can then learn the latest and greatest models even on your own!
All the resources for this course are available at: https://github.com/PacktPublishing/Probability-Statistics---The-Foundations-of-Machine-Learning
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