#1 Basic Math for Data Science ( Hands-on ) | Python смотреть онлайн

With the availability of data, there is a growing demand for talent who can analyze and make sense of it. This makes practical math all the more important because it helps infer insights from data. However, mathematics comprises many topics, and it is hard to identify which ones are applicable and relevant for a data science career. Knowing these essential math topics is key to integrating knowledge across data science, statistics, and machine learning.

You’ll grasp the fundamentals of probability, statistics, hypothesis testing, linear algebra, linear regression, classification models, and practical calculus. Along the way you’ll integrate what you’ve learned and see practical applications for real-world problems.

But before we start with actual mathematics topics like Algebra, we should have some understanding of the basic concepts. This video will help you sharpen your basic mathematical skills which in turn will help you a lot in coping with some of the mathematically intense parts of this course.

-----------------------------------------------------------------------------------------------------------------------------------------------------------
Chapters
00:00 Intro
00:59 Arithmetic printing with nice formatting
02:34 Introduction to latex coding
6:52 Difference between Symbolic & Numerical Math
11:55 Integrate Sympy & Latex
13:17 Difference between Lists & Numpy Arrays

-----------------------------------------------------------------------------------------------------------------------------------------------------------

If you liked the video Please Do Subscribe My Channel ???

▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
Thank you for Watching!
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬