Math for Data Science and Machine Learning: University Level

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Math for Data Science and Machine Learning: University Level


In this course, we will learn math for data science and machine learning. We will also discuss the importance of Math for data science and machine learning in practical words. Moreover, Math for data science and machine learning course is a bundle of two courses of linear algebra and probability and statistics. So, students will learn the complete contents of probability and statistics, and linear algebra. It is not like that you will not complete all the contents in this 7 hours video course. This is a beautiful course and I have designed this course according to the need of the students.


Linear algebra and probability and statistics is usually offered for the students of data science, machine learning, python, and IT students. So, that’s why I have prepared this dual course for different sciences.


I have taught this course multiple times in my university classes. It is offered usually in two different modes like it is offered as linear algebra for 100 marks paper and probability and statistics as another 100 marks paper for two different or in the same semesters. I usually focus on the method and examples while teaching this course. Examples clear the concepts of the students in a variety of ways like, they can understand the main idea that instructor wants to deliver if they feel typical the method of the subject or topics. So, focusing on examples makes the course easy and understandable for the students.

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Many instructors (not kidding anyone but it is reality) put the 30 + hours just on one topic like linear algebra, which I think is useless. Students don’t have the time to see the huge videos. So, that’s why I am giving the two kinds of stuff in one stuff (2 in 1)linear algebra and probability and statistics. The complete course is very highly recognized and all the videos are high definition videos.


In linear algebra, the students will master the concepts of matrix and determinant, solution of nonlinear equations by different methods, vector spaces, linearly dependent and independent set of vectors, linear transformation, and Gram’s Schmidt normalization process.


While in Probability and Statistics, the students will learn sample spaces, distributions, mean, median, mode, and range. They will also learn the other contents of probability and statistics in a detailed way.


To see the complete contents, please visits the contents sections of this course. The videos are relatively long videos that start from 10 minutes and end in 50 minutes. And the course has been designed on PowerPoint slides. All the concepts have been illustrated with the mouse cursor on the slides. Just follow the voice-over and the mouse cursor to understand the concepts.


Here are some reviews of my courses by the students.

1- Brava Man:  Superb course!!

The instructor is very knowledgeable and presents the Quantum Physics concepts in a detailed and methodical way.

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We walked through aspects like doing research and implementation via examples that we can follow in addition, to actual mathematical problems we are presented to solve.

2- Manokaran Masikova: This is a good course to learn about quantum mechanics from basic and he explained with example to understand the concept.

3- Dr. B Baskaran: very nice to participate in the course and very much interesting and useful also.

4- Mashrur Bhuiyan: Well currently i am an Engineering student and I forgot the basics of my calculus. but this course helped me to get a good understanding of differentiation and integration. Overall all of the teaching methods is good.

5- Kaleem Ul Haq: Really a great explanation and each step has explained well. I am enjoying this course. He is a familiar instructor in calculus. I have seen many lectures of this instructor before taking this course.

Thanks for reading the description of this course. Hope you will join me in this course. Have a nice day and wish you good luck.

Who this course is for:

  • Students of engineering, data science, python and machine learning



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