Math for Data Science and Machine Learning

Description

• < Step-by-step explanation of more than 6.5 hours of video lessons on Math for Data Science and Machine Learning>

• <Instant reply to your questions asked during lessons>

• <Weekly live talks on Math for Data Science and Machine Learning. You can raise your questions in a live session as well>

• <Helping materials like notes, examples, and exercises>

• <Solution of quizzes and assignments>

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 in linear algebra and probability and statistics. So, students will learn the complete contents of probability and statistics and linear algebra. It is not like 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.

WHERE THIS COURSE IS APPLICABLE?

Linear algebra and probability and statistics are usually offered for students of data science, machine learning, python, and IT students. So, that's why I have prepared this dual course for 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 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, they can understand the main idea that the 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.

2 IN 1 STUFF

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.

LINEAR ALGEBRA SECTIONS INCLUDE

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 sets of vectors, linear transformation, and Gram's Schmidt normalization process.

PROBABILITY AND STATISTICS SECTIONS INCLUDE

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

What You Will Learn!

• Introduction to matrix
• Gauss's elimination method
• Properties of matrix and determinants
• Echelon and reduce echelon form
• Vector spaces
• Linearly dependent and independent set of vectors in vector spaces
• Basis of a vector space
• linear transformation and related example and exercises
• Inner product spaces
• Eigen values and eigen vectors
• Introduction to ODE's and PDE's
• Probability and statistics
• What is a sample space?
• Mean, median and mode for grouped and ungrouped data
• Poison, gamma and uniform distributions
• And many more probability and statistics related tutorials
• Gram's Schmidt orthonormal process
• Pi chart, bar graph, line graph and histogram
• Permutations and Combinations
• Sets and Venn Diagram

Who Should Attend!

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

TAKE THIS COURSE