Packt | Mathematics for Data Science and Machine Learning using R [FCO]

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1.Introduction
  • 01.Intro.mp4 (12.2 MB)
2.Overview of R
  • 02.Introduction.mp4 (11.0 MB)
  • 03.Overview of R Workspace & Basic Commands.mp4 (357.1 MB)
  • 04.LAB 1 Intro.mp4 (26.7 MB)
  • 05.LAB 1 Solution.mp4 (195.6 MB)
3.Linear Algebra
  • 06.Scalars Vectors and Matrices.mp4 (91.6 MB)
  • 07.Application Scalars Vectors and Matrices.mp4 (92.6 MB)
  • 08.LAB 1 Intro Scalars Vectors and Matrices.mp4 (5.2 MB)
  • 09.LAB 1 Solution Scalars Vectors and Matrices.mp4 (68.2 MB)
  • 10.Vector Operations.mp4 (32.0 MB)
  • 11.Application Vector Operations.mp4 (131.7 MB)
  • 12.LAB 2 Intro Vector Operations.mp4 (5.7 MB)
  • 13.LAB 2 Solution Vector Operations.mp4 (68.2 MB)
  • 14.Matrix Operations Addition Subtraction Multiplication.mp4 (48.6 MB)
  • 15.Application Matrix Operations Addition Subtraction Multiplication.mp4 (53.5 MB)
  • 16.LAB 3 Intro Matrix Operations Addition Subtraction Multiplication.mp4 (3.6 MB)
  • 17.LAB 3 Solution Matrix Operations Addition Subtraction Multiplication.mp4 (21.1 MB)
  • 18.Matrix Operations Transposes and Inverses.mp4 (31.9 MB)
  • 19.Application Matrix Operations Transposes and Inverses.mp4 (60.6 MB)
  • 20.LAB 4 Intro Matrix Operations Transposes and Inverses.mp4 (2.7 MB)
  • 21.LAB 4 Solution Matrix Operations Transposes and Inverses.mp4 (16.5 MB)
  • 22.What is Linear Regression.mp4 (44.5 MB)
  • 23.Application What is Linear Regression.mp4 (175.5 MB)
  • 24.LAB 5 Intro What is Linear Regression.mp4 (10.8 MB)
  • 25.Lab 5 Solution What is Linear Regression.mp4 (61.0 MB)
  • 26.Matrix Representation of Linear Regression.mp4 (20.7 MB)
  • 27.Application Matrix Representation of Linear Regression.mp4 (78.0 MB)
  • 28.Lab 6 Intro Matrix Representation of Linear Regression.mp4 (11.8 MB)
  • 29.Lab 6 Solution Matrix Representation of Linear Regression.mp4 (57.1 MB)
4.Section Calculus
  • 30.Functions and Tangent Lines.mp4 (134.9 MB)
  • 31.Application Functions and Tangent Lines.mp4 (61.6 MB)
  • 32.Lab 1 Intro Functions and Tangent Lines.mp4 (4.2 MB)
  • 33.Lab 1 Solution Functions and Tangent Lines.mp4 (45.1 MB)
  • 34.Derivatives.mp4 (28.5 MB)
  • 35.Application Derivatives.mp4 (107.8 MB)
  • 36.Lab 2 Intro Derivatives.mp4 (6.9 MB)
  • 37.Lab 2 Solution Derivatives.mp4 (83.5 MB)
  • 38.Optimization Using Derivatives Single Variable Functions.mp4 (41.3 MB)
  • 39.Application Optimization Using Derivatives Single Variable.mp4 (52.9 MB)
  • 40.Intro Optimization Using Derivatives Single Variable Function.mp4 (4.5 MB)
  • 41.Lab 3 Solution Optimization Using Derivatives Single Variable Function.mp4 (50.9 MB)
  • 42.Optimization Using Derivatives Two Variable Functions.mp4 (40.2 MB)
  • 43.Application Optimization Using Derivatives Two Variable Function.mp4 (82.8 MB)
  • 44.Lab 4 Intro Optimization Using Derivatives Two Variable Functions.mp4 (7.8 MB)
  • 45.Lab 4 Solution Optimization Using Derivatives Two Variable Function.mp4 (25.0 MB)
  • 46.Linear Regression the Calculus Optimization Perspective.mp4 (61.6 MB)
  • 47.Application Linear Regression the Calculus Optimization Perspective.mp4 (35.9 MB)
  • 48.Lab 5 Intro Linear Regression the Calculus Optimization Perspective.mp4 (9.3 MB)
  • 49.Lab 5 Solution Linear Regression the Calculus Optimization Perspective.mp4 (32.9 MB)
5.Tying it All Together Vector Calculus
  • 50.Orthogonal Vectors and Linear Independence.mp4 (110.4 MB)
  • 51.Application Orthogonal Vectors and Linear Independence.mp4 (28.8 MB)
  • 52.Lab 1 Intro Orthogonal Vectors and Linear Independence.mp4 (9.2 MB)
  • 53.Lab 1 Solution Orthogonal Vectors and Linear Independence.mp4 (28.9 MB)
  • 54.Eigenvectors and Eigenvalues.mp4 (40.4 MB)
  • 55.Application Eigenvectors and Eigenvalues.mp4 (260.0 MB)
  • 56.Lab 2 Intro Eigenvectors and Eigenvalues.mp4 (1.9 MB)
  • 57.Lab 2 Solution Eigenvectors and Eigenvalues.mp4 (70.3 MB)
  • 58.Vectors Gradient Descent.mp4 (106.1 MB)
  • 59.Application Vectors Gradient Descent.mp4 (167.0 MB)
  • 60.Lab 3 Intro Vectors Gradient Descent.mp4 (12.5 MB)
  • 61.Lab 3 Solution Vectors Gradient Descent.mp4 (27.5 MB)
  • 62.Linear Regression the Gradient Descent Perspective.mp4 (43.3 MB)
  • 63.Application Linear Regression the Gradient Descent Perspective.mp4 (35.9 MB)
  • 64.Lab 4 Intro Linear Regression the Gradient Descent Perspective.mp4 (12.4 MB)
  • 65.Lab 4 Solution Linear Regression the Gradient Descent Perspective.mp4 (96.7 MB)
Exercise Files
  • code_9781839210945.zip (118.8 MB)

Description

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By : Eduonix
Released : September 27, 2019 (New Release!)

Learn the basic math for Data Science, AI, and ML using R

Video Details

ISBN 9781839210945
Course Length 11 hours 57 minutes

Learn

• Master the basic math concepts you need for data science and Machine Learning
• Learn to implement mathematical concepts using R
• Master linear algebra, calculus, and vector calculus from the ground up
• Master the R programming language

About

With data increasing every day, Data Science has become one of the most essential aspects in most fields. From healthcare to business, data is essential everywhere. However, it revolves around three major aspects: data itself, foundational concepts, and programming languages that interpret data.
This course teaches you everything you need to know about the basic math for Data Science via the R programming language, developed specifically to perform statistics and data analytics and utilize graphical modules more effectively.

Data Science has become an interdisciplinary field that deals with the processes and systems used to extract knowledge or make predictions from large amounts of data. From helping brands to understand their customers to solve complex IT problems, its usability in almost every other field makes it very important for the functioning and growth of organizations or companies. We supply an overview of Machine Learning and the R programming language, linear algebra- scalars, vectors, matrices, linear regression, calculus-tangents, derivatives, vector calculus, vector spaces, Gradient Descent, and others.

All the codes and supporting files for this course are available at – https://github.com/PacktPublishing/Mathematics-for-Data-Science-and-Machine-Learning-using-R

Features:

• Understand linear algebra – scalars, vectors, and matrices
• Understand the fundamental mathematics for data science, AI, and ML using R.





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Packt | Mathematics for Data Science and Machine Learning using R [FCO]

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Packt | Mathematics for Data Science and Machine Learning using R [FCO]

Torrent hash: A8212B8DD6D6A2E73A5034BEF7C499DCB4941554