Calculus & Linear Algebra for Machine Learning from Scratch

Calculus & Linear Algebra for Machine Learning from Scratch, available at $44.99, has an average rating of 4.8, with 38 lectures, 2 quizzes, based on 53 reviews, and has 252 subscribers.

You will learn about Functions Differentiation Integration Partial Differentiation Basics of Matrices Addition and Subtraction of Matrices Scalar Multiplication of Matrices Mutiplication of Matrices Vectors Finding Determinant of a Matrix Inverse of a Matrix Eigenvalues and Eigenvectors Linear regression Application of Mathematics in Data Science and Machine Learning This course is ideal for individuals who are Beginner Data Science Students or Students facing difficulty in understanding Mathematical prerequisites of Python and Data Science It is particularly useful for Beginner Data Science Students or Students facing difficulty in understanding Mathematical prerequisites of Python and Data Science.

Enroll now: Calculus & Linear Algebra for Machine Learning from Scratch

Summary

Title: Calculus & Linear Algebra for Machine Learning from Scratch

Price: $44.99

Average Rating: 4.8

Number of Lectures: 38

Number of Quizzes: 2

Number of Published Lectures: 38

Number of Published Quizzes: 2

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 40

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

Functions
Differentiation
Integration
Partial Differentiation
Basics of Matrices
Addition and Subtraction of Matrices
Scalar Multiplication of Matrices
Mutiplication of Matrices
Vectors
Finding Determinant of a Matrix
Inverse of a Matrix
Eigenvalues and Eigenvectors
Linear regression
Application of Mathematics in Data Science and Machine Learning

Who Should Attend

Beginner Data Science Students
Students facing difficulty in understanding Mathematical prerequisites of Python and Data Science

Target Audiences

Beginner Data Science Students
Students facing difficulty in understanding Mathematical prerequisites of Python and Data Science

In this course you will learn Calculus and Linear Algebra for Machine Learning and Data Science from scratch.

Calculus and Linear Algebra form the foundation of the essential Mathematical background required in the field of Machine Learning, Deep Learning and Data Science.

In this course you will learn…

Functions

Differentiation

Integration

Partial Differentiation

Basics of Matrices

Addition and Subtraction of matrices

Multiplication of Matrices

Vectors

Eigenvalues and Eigenvectors

Application of Mathematics in Data Science

Calculus & Linear Algebra finds wide variety of applications in different fields of Machine Learning and Data Science. B Learning Calculus & Linear Algebra will help you in understanding advanced topics of Machine Learning and Data Science. If you are taking a Machine Learning or Data Science course, then this course is certainly going to help you.

In this course I have provided video lectures for each and every concept, these lectures are supplemented by quizzes and examples that will help you reinforcing your knowledge and will also help you in learning the Math behind Machine Learning.

Course Curriculum

Chapter 1: Calculus

Lecture 1: Functions

Lecture 2: Applications of Functions

Lecture 3: Functions-Challenges

Lecture 4: Types of functions

Lecture 5: Introduction to Differentiation

Lecture 6: Differentiation of functions

Lecture 7: Differentiation-Example

Lecture 8: Product Rule

Lecture 9: Quotient Rule

Lecture 10: Chain Rule

Lecture 11: Chain Rule – Example

Lecture 12: Introduction to Integration

Lecture 13: Some common Integrals

Lecture 14: Integration – Example

Lecture 15: Partial Differentiation

Lecture 16: Partial Differentiation – Example

Chapter 2: Linear Algebra

Lecture 1: Introduction

Lecture 2: Index of a Matrix

Lecture 3: Transpose of a Matrix

Lecture 4: Types of Matrices

Lecture 5: Addition and Subtraction of Matrices

Lecture 6: Scalar multiplication of Matrices

Lecture 7: Vectors

Lecture 8: Multiplication of Matrices

Lecture 9: Check Point

Lecture 10: Determinant of a Matrix

Lecture 11: Inverse of a Matrix

Lecture 12: Eigenvalues and Eigenvectors

Chapter 3: Applications of Linear Algebra in ML& Data Science : Linear regression

Lecture 1: Introduction to Machine Learning

Lecture 2: Linear Regression

Lecture 3: Linear Regression: Normal Equation

Lecture 4: Linear Regression: Gradient Descent

Chapter 4: Machine Learning Project

Lecture 1: Loading the data

Lecture 2: Organizing the data

Lecture 3: Splitting the data into train and test dataset

Lecture 4: Creating a model and training it

Lecture 5: Evaluating the trained model

Lecture 6: Making Predictions

Instructors

Ajatshatru Mishra
Data Scientist

Rating Distribution

1 stars: 0 votes
2 stars: 2 votes
3 stars: 9 votes
4 stars: 15 votes
5 stars: 27 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!

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