Maths behind machine learning

Maths behind machine learning, available at Free, has an average rating of 3.45, with 25 lectures, based on 50 reviews, and has 6575 subscribers.

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You will learn about Students will get an opportunity to explore the complex mathematics behind machine learning algorithms. Students will be able to write machine learning algorithms from scratch. Students will get guidance on how to build in the knowledge they gained in the course. Student will be receive life-long access to the course for future reference. This course is ideal for individuals who are High-schoolers and freshmen with an urge to explore machine learning. It is particularly useful for High-schoolers and freshmen with an urge to explore machine learning.

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Summary

Title: Maths behind machine learning

Price: Free

Average Rating: 3.45

Number of Lectures: 25

Number of Published Lectures: 25

Number of Curriculum Items: 25

Number of Published Curriculum Objects: 25

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Students will get an opportunity to explore the complex mathematics behind machine learning algorithms.

Students will be able to write machine learning algorithms from scratch.

Students will get guidance on how to build in the knowledge they gained in the course.

Student will be receive life-long access to the course for future reference.

Who Should Attend

High-schoolers and freshmen with an urge to explore machine learning.

Target Audiences

High-schoolers and freshmen with an urge to explore machine learning.

This course is for students who are looking for logic behind the myriad of machine learning algorithms they use every day. When I started my journey with machine learning, it was really difficult for me to intuitively understand the code I was writing. However, after watching multiple videos and reading millions of articles, I finally understood the fundamentals of machine learning algorithms. In this course, I’ll walk you through the mathematical concepts you need to know to understand and implement a machine learning algorithm. Other than that. you’ll also learn how to build the same algorithms from scratch using python. No kind of libraries will be imported during the course. This will help you in understanding the algorithm properly as none of the work will be taking place in the background. This course does not feature high-level machine algorithms instead it focuses on the most basic ones: bivariate regression, multivariate regression, support vector regression, k-nearest neighbors. The scope of this course will gradually expand and soon it will feature tutorials on techniques like deep neural networks. This course is a condensed version of my knowledge which I gained through multiple resources. You are free to drop in your queries in the Q&A section, I will be glad to resolve them. Happy coding 馃槈

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Regression

Lecture 1: Introduction to Regression

Lecture 2: RSS (Bivariate regression)

Lecture 3: Graphical Intuition (Bivariate regression)

Lecture 4: Minimising RSS (Bivariate regression)

Lecture 5: Code along (Bivariate regression)

Lecture 6: Solved example (Bivariate regression)

Lecture 7: Multivariate Regression

Lecture 8: Graphical Intuition (Multivariate regression)

Lecture 9: Minimising RSS (Mulvariate regression)

Lecture 10: Code along (Multivariate regression)

Lecture 11: Solved example (Multivariate regression)

Chapter 3: Support Vector Machines

Lecture 1: Introduction SVM

Lecture 2: Test data

Lecture 3: Width expression

Lecture 4: Minimising width expression

Lecture 5: Code along (SVM)

Lecture 6: Solved example (SVM)

Lecture 7: Additional Notes

Chapter 4: K nearest neighbours

Lecture 1: K-nn introduction

Lecture 2: Graphical Intuition (K-nn)

Lecture 3: Formulating distance matrix

Lecture 4: Code along (K-nn)

Lecture 5: Solved example (K-nn)

Chapter 5: Conclusion

Lecture 1: Conclusion

Instructors

Samyak Jain
Student

Rating Distribution

1 stars: 8 votes

2 stars: 6 votes

3 stars: 14 votes

4 stars: 10 votes

5 stars: 12 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!