Statistics & Linear Algebra for Machine Learning

Math Behind ML Algorithms | Linear Algebra | Hypothesis Testing | ANOVA

4.00 (246 reviews)
Udemy
platform
English
language
Other
category
instructor
23,135
students
3.5 hours
content
Oct 2023
last update
$69.99
regular price

What you will learn

You will understand the fundamentals of mathematics and statistics relevant for machine learning

You will gain insights on the application of math and stats on machine learning

You will know what problems Machine Learning can solve, and how the Machine Learning Process works

You will learn Measures of Central Tendency vs Dispersion

You will understand Mean vs Standard Deviation & Percentiles

You will have clarity on the Types of Data & Dependent vs independent variables

You will be knowledgeable on Probability & Sample Vs population

You will gain clarity on Hypothesis testing

You will learn the Types of distribution & Outliers

You will understand the maths behind algorithms like regression, decision tree and kNN

You will gain insights on optimization and gradient descent

Description

Testimonials about the course

  • "Great course. It cleared all my doubts. I learned statistics previously from HK Dass sir's book, but I couldn't understand there relationship in data science and machine learning. Loved this course!" Rubayet A.

  • "Simply amazing course where every basics are described clearly and precisely. Go for this course." Dipesh S

  • "Es claro, preciso en los datos. Las ilustraciones son muy pedagógicas, sobre todo las analogías." . Héctor Marañón R.

  • "Good for beginners like me to learn the concepts of Machine Learning and the math behind of it. Great to review this course again. Thanks." Clark D

  • "Excelentes conceptos, enfocados hacia las investigaciín de base científica" Oscar M

Background and Introduction

The trainer of this course is an AI expert and he has observed that many students and young professionals make the mistake of learning machine learning without understanding the core concepts in maths and statistics. This course will help to address that gap in a big way.

Since Machine Learning is a field at the intersection of multiple disciplines like statistics, probability, computer science, and mathematics, its essential for practitioners and budding enthusiasts to assimilate these core concepts.

These concepts will help you to lay a strong foundation to build a thriving career in artificial intelligence.

This course teaches you the concepts mathematics and statistics but from an application perspective. It’s one thing to know about the concepts but it is another matter to understand the application of those concepts. Without this understanding, deploying and utilizing machine learning will always remain challenging.

You will learn concepts like measures of central tendency vs dispersion, hypothesis testing, population vs sample, outliers and many interesting concepts. You will also gain insights into gradient decent and mathematics behind many algorithms.

We cover the below concepts in this course:

  • Measures of Central Tendency vs Dispersion

  • Mean vs Standard Deviation

  • Percentiles

  • Types of Data

  • Dependent vs independent variables

  • Probability

  • Sample Vs population

  • Hypothesis testing

  • Concept of stability

  • Types of distribution

  • Outliers

  • Maths behind machine learning algorithms like regression, decision tree and kNN

  • Gradient descent.

  • Arrays

  • Vectors

  • Dot product

  • Magnitude

  • Eigen vector, eigen value

  • Cosine similarity ...


Content

Introduction

Introduction

Applied Stats 1

Applied Stats 1

Applied Stats 2

Applied Stats 2

Applied Stats 3

Applied Stats 3

Conclusion

Conclusion

Screenshots

Statistics & Linear Algebra for Machine Learning - Screenshot_01Statistics & Linear Algebra for Machine Learning - Screenshot_02Statistics & Linear Algebra for Machine Learning - Screenshot_03Statistics & Linear Algebra for Machine Learning - Screenshot_04

Reviews

Ivan
July 19, 2023
A lot of the information is not explained, it looks like a course for someone who already knows the subject.
Rubayet
September 3, 2021
Great course. It cleared all my doubts. I learned statistics previously from HK Dass sir's book, but I couldn't understand there relationship in data science and machine learning. Loved this course!
Clark
September 2, 2021
Good for beginners like me to learn the concepts of Machine Learning and the math behind of it. Great to review this course again. Thanks.
Annapoorani
May 11, 2021
Hi Thanks for Udemy for providing this course!! This course helped me to understand the basic maths concepts behind the algorithm. There are so many algorithms as Random Forest, Apriori etc. Would like to know about them also. Regards, Annapoorani
Javokhir
March 19, 2021
You'll have general understand of complex topics and algorithms. Course is very comprehensive and provides really life examples. The complex consepts explained in the best possible and most easy way to follow. You don't get confused thoughout the course.
MUHAMMAD
February 18, 2021
The course has amazing intuition/concepts on basics ML algorithms. Recommended for absolute beginners.
Shreya
December 26, 2020
I enjoyed the course and liked the conversational style of presentation. The course covered core fundamental concepts which are needed for machine learning.
Hiral
December 14, 2020
Title is misleading. It should be "Introduction to Applied Math & Statistics for Machine Learning" . This is a webex recording of some online session where presenter interacts with audience but we get to hear only the presenter. This causes delays, disconnects, unknown moments of silence etc. Concepts of applied math and statistics are covered at an absolute high level. Not much helpful as the title of course sets a different expectation.

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3559993
udemy ID
10/10/2020
course created date
10/20/2020
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