Duration: 6h*50m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 1.81 GB
Genre: eLearning | Language: EnglishLearn data science and machine learning math, math f artificial intelligence, css, html and computer science
What you'll learn




Introduction to matrix
Gauss's elimination method
Properties of matrix and determinants
Echelon and reduce echelon fm
Vect spaces
Linearly dependent and independent set of vects in vect spaces
Basis of a vect space
linear transfmation and related example and exercises
Inner product spaces
Eigen values and eigen vects
Introduction to ODE's and PDE's
Probability and statistics
What is a sample space?
Mean, median and mode f grouped and ungrouped data
Poison, gamma and unifm distributions
And many me probability and statistics related tutials
Gram's Schmidt thonmal process
Pi chart, bar graph, line graph and histogram
Permutations and Combinations
Sets and Venn Diagram

Requirements
Basics of high school mathematics
Description
In this course, we will learn math f data science and machine learning. We will also discuss the imptance of Math f data science and machine learning in practical wds. Meover, Math f 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 accding to the need of the students.

WHERE THIS COURSE IS APPLICABLE?

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

METHODOLOGY

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 f 100 marks paper and probability and statistics as another 100 marks paper f two different 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 instruct wants to deliver if they feel typical the method of the subject topics. So, focusing on examples makes the course easy and understandable f the students.

2 IN 1 STUFF

Many instructs (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.

LINEAR ALGEBRA SECTIONS INCLUDES

In linear algebra, the students will master the concepts of matrix and determinant, solution of nonlinear equations by different methods, vect spaces, linearly dependent and independent set of vects, linear transfmation, and Gram's Schmidt nmalization process.

PROBABILITY AND STATISTICS SECTIONS INCLUDES

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.

THE COMPLETE DETAIL OF CONTENTS

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 curs on the slides. Just follow the voice-over and the mouse curs to understand the concepts.

WHAT PEOPLE ASK ABOUT MY COURSES

Here are some reviews of my courses by the students.

1- Brava Man: Superb course!!

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

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 fgot 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 instruct in calculus. I have seen many lectures of this instruct befe taking this course.

Thanks f 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 f:
Students of engineering, data science, python and machine learning

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