Mathematics for Data Science and Machine Learning using R

Mathematics for Data Science and Machine Learning using R, available at $54.99, has an average rating of 4.3, with 65 lectures, based on 119 reviews, and has 950 subscribers.

You will learn about Master the fundamental mathematical concepts required for Datas Science and Machine Learning Learn to implement mathematical concepts using R Master Linear alzebra, Calculus and Vector calculus from ground up Master R programming langauge This course is ideal for individuals who are Anyone who wants to learn Data Science , AI or Machine learning will find this course very useful It is particularly useful for Anyone who wants to learn Data Science , AI or Machine learning will find this course very useful.

Enroll now: Mathematics for Data Science and Machine Learning using R

Summary

Title: Mathematics for Data Science and Machine Learning using R

Price: $54.99

Average Rating: 4.3

Number of Lectures: 65

Number of Published Lectures: 65

Number of Curriculum Items: 65

Number of Published Curriculum Objects: 65

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will Learn

Master the fundamental mathematical concepts required for Datas Science and Machine Learning
Learn to implement mathematical concepts using R
Master Linear alzebra, Calculus and Vector calculus from ground up
Master R programming langauge

Who Should Attend

Anyone who wants to learn Data Science , AI or Machine learning will find this course very useful

Target Audiences

Anyone who wants to learn Data Science , AI or Machine learning will find this course very useful

With the increase of data by each passing day, Data Science has become one of the most important aspects in most of the fields. From healthcare to business, everywhere data is important. However, it revolves around 3 major aspects i.e. data, foundational concepts and programming languages for interpreting the data. This course teaches you everything about all the foundational mathematics for Data Science using R programming language, a language developed specifically for performing statistics, data analytics and graphical modules in a better way.

Why Learn Foundational mathematical Concepts for Data Science Using R?

Data Science has become an interdisciplinary field which deals with processes and systems used for extracting knowledge or making predictions from large amounts of data. Today, it has become an integral part of numerous fields resulting in the high demand of professionals of data science. From helping brands to understand their customers, solving complex IT problems, to its usability in almost every other field makes it very important for the functioning and growth of any organizations or companies. Depending upon the location the average salary of data scientist expert can be over $120,000. This course will help you learn the concepts the correct way.

Why You Should Take This Online Tutorial?

Despite the availability of several tutorials on data science, it is one of the online guides containing hand-picked topics on the concepts for foundational mathematics for Data Science using R programming language. It includes myriads of sections (over 9 hours of video content) lectured by Timothy Young, a veteran statistician and data scientists . It explains different concepts in one of the simplest form making the understanding of Foundational mathematics for Data Science very easy and effective.

This Course includes:

Overview of Machine Learning and R programming language
Linear Algebra- Scalars, vectors & Metrices
Vector and Matrix Operations
Linear Regression
Calculus- Tangents, Derivatives and others
Vector Calculus- Vector spaces, Gradient Descent and others
So Much More!

This field is constantly become important for both industries as well as developers. If you are one of those who loves data science and are having issues with all the foundational concepts related to it, then it’s the right online tutorial to solve your issues. Start today, in order to become the expert of tomorrow!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Intro

Chapter 2: Overview of R

Lecture 1: Introduction

Lecture 2: Overview of R Workspace & Basic Commands

Lecture 3: LAB 1 Intro

Lecture 4: LAB 1 Solution

Chapter 3: Linear Algebra

Lecture 1: Scalars Vectors and Matrices

Lecture 2: LAB 1 Intro Scalars Vectors and Matrices

Lecture 3: Application Scalars Vectors and Matrices

Lecture 4: LAB 1 Solution Scalars Vectors and Matrices

Lecture 5: Vector Operations

Lecture 6: Application Vector Operations

Lecture 7: LAB 2 Intro Vector Operations

Lecture 8: LAB 2 Solution Vector Operations

Lecture 9: Matrix Operations Addition Subtraction Multiplication

Lecture 10: Application Matrix Operations Addition Subtraction Multiplication

Lecture 11: LAB 3 Intro Matrix Operations Addition Subtraction Multiplication

Lecture 12: LAB 3 Solution Matrix Operations Addition Subtraction Multiplication

Lecture 13: Matrix Operations Transposes and Inverses

Lecture 14: Application Matrix Operations Transposes and Inverses

Lecture 15: LAB 4 Intro Matrix Operations Transposes and Inverses

Lecture 16: LAB 4 Solution Matrix Operations Transposes and Inverses

Lecture 17: What is Linear Regression

Lecture 18: Application What is Linear Regression

Lecture 19: LAB 5 Intro What is Linear Regression

Lecture 20: Lab 5 Solution What is Linear Regression

Lecture 21: Matrix Representation of Linear Regression

Lecture 22: Application Matrix Representation of Linear Regression

Lecture 23: Lab 6 Intro Matrix Representation of Linear Regression

Lecture 24: Lab 6 Solution Matrix Representation of Linear Regression

Chapter 4: Section Calculus

Lecture 1: Functions and Tangent Lines

Lecture 2: Application Functions and Tangent Lines

Lecture 3: Lab 1 Intro Functions and Tangent Lines

Lecture 4: Lab 1 Solution Functions and Tangent Lines

Lecture 5: Derivatives

Lecture 6: Application Derivatives

Lecture 7: Lab 2 Intro Derivatives

Lecture 8: Lab 2 Solution Derivatives

Lecture 9: Optimization Using Derivatives Single Variable Functions

Lecture 10: Application Optimization Using Derivatives Single Variabl

Lecture 11: Intro Optimization Using Derivatives Single Variable Function

Lecture 12: Lab 3 Solution Optimization Using Derivatives Single Variable Funct

Lecture 13: Optimization Using Derivatives Two Variable Functions

Lecture 14: Application Optimization Using Derivatives Two Variable F

Lecture 15: Lab 4 Intro Optimization Using Derivatives Two Variable Functions

Lecture 16: Lab 4 Solution Optimization Using Derivatives Two Variable Function

Lecture 17: Linear Regression The Calculus Optimization Perspective

Lecture 18: Application Linear Regression The Calculus Optimization P

Lecture 19: Lab 5 Intro Linear Regression The Calculus Optimization Perspective

Lecture 20: Lab 5 Solution Linear Regression The Calculus Optimization Perspect

Chapter 5: Tying it All Together Vector Calculus

Lecture 1: Orthogonal Vectors and Linear Independence

Lecture 2: Application Orthogonal Vectors and Linear Independence

Lecture 3: Lab 1 Intro Orthogonal Vectors and Linear Independence

Lecture 4: Lab 1 Solution Orthogonal Vectors and Linear Independence

Lecture 5: Eigenvectors and Eigenvalues

Lecture 6: Application Eigenvectors and Eigenvalues

Lecture 7: Lab 2 Intro Eigenvectors and Eigenvalues

Lecture 8: Lab 2 Solution Eigenvectors and Eigenvalues

Lecture 9: Vectors Gradient Descent

Lecture 10: Application Vectors Gradient Descent

Lecture 11: Lab 3 Intro Vectors Gradient Descent

Lecture 12: Lab 3 Solution Vectors Gradient Descent

Lecture 13: Linear Regression The Gradient Descent Perspective

Lecture 14: Application Linear Regression The Gradient Descent Perspective

Lecture 15: Lab 4 Intro Linear Regression The Gradient Descent Perspectivve

Lecture 16: Lab 4 Solution Linear Regression The Gradient Descent Perspective

Instructors

Eduonix Learning Solutions
1+ Million Students Worldwide | 200+ Courses
Eduonix-Tech .

Rating Distribution

1 stars: 1 votes
2 stars: 2 votes
3 stars: 14 votes
4 stars: 40 votes
5 stars: 62 votes

Frequently Asked Questions

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

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