Data Science and Machine Learning Bootcamp with R

Data Science and Machine Learning Bootcamp with R
Data Science and Machine Learning Bootcamp with R
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 17.5 Hours | 2.39 GB

Learn how to use the R programming language for data science and machine learning and data visualization!

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!

This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

We’ll teach you how to program with R, how to create amazing data visualizations, and how to use Machine Learning with R! Here a just a few of the topics we will be learning:

  • Programming with R
  • Advanced R Features
  • Using R Data Frames to solve complex tasks
  • Use R to handle Excel Files
  • Web scraping with R
  • Connect R to SQL
  • Use ggplot2 for data visualizations
  • Use plotly for interactive visualizations
  • Machine Learning with R, including:
  • Linear Regression
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Data Mining Twitter
  • Neural Nets and Deep Learning
  • Support Vectore Machines
  • and much, much more!

What you’ll learn

  • Program in R
  • Use R for Data Analysis
  • Create Data Visualizations
  • Use R to handle csv, excel, SQL files or web scraping
  • Use R to manipulate data easily
  • Use R for Machine Learning Algorithms
  • Use R for Data Science
Table of Contents

Course Introduction
1 Introduction to Course
2 Course Curriculum
3 What is Data Science
4 Course FAQ

Course Best Practices
5 How to Get Help in the Course!
6 Installation and Set-Up

Windows Installation Set-Up
7 Windows Installation Procedure

Mac OS Installation Set-Up
8 Mac OS Installation Procedure

Linux Installation
9 LinuxUnbuntu Installation Procedure

Development Environment Overview
10 Development Environment Overview
11 Course Notes
12 Guide to RStudio

Introduction to R Basics
13 Introduction to R Basics
14 R Basics Training Exercise
15 R Basics Training Exercise – Solutions Walkthrough
16 Arithmetic in R
17 Variables
18 R Basic Data Types
19 Vector Basics
20 Vector Operations
21 Comparison Operators
22 Vector Indexing and Slicing
23 Getting Help with R and RStudio

R Matrices
24 Introduction to R Matrices
25 Creating a Matrix
26 Matrix Arithmetic
27 Matrix Operations
28 Matrix Selection and Indexing
29 Factor and Categorical Matrices
30 Matrix Training Exercise
31 Matrix Training Exercises – Solutions Walkthrough

R Data Frames
32 Introduction to R Data Frames
33 Data Frame Basics
34 Data Frame Indexing and Selection
35 Overview of Data Frame Operations – Part 1
36 Overview of Data Frame Operations – Part 2
37 Data Frame Training Exercise
38 Data Frame Training Exercises – Solutions Walkthrough

R Lists
39 List Basics

Data Input and Output with R
40 Introduction to Data Input and Output with R
41 CSV Files with R
42 Excel Files with R
43 SQL with R
44 Web Scraping with R

R Programming Basics
45 Introduction to Programming Basics
46 Functions Training Exercise – Solutions
47 Logical Operators
48 if, else, and else if Statements
49 Conditional Statements Training Exercise
50 Conditional Statements Training Exercise – Solutions Walkthrough
51 While Loops
52 For Loops
53 Functions
54 Functions Training Exercise

Advanced R Programming
55 Introduction to Advanced R Programming
56 Built-in R Features
57 Apply
58 Math Functions with R
59 Regular Expressions
60 Dates and Timestamps

Data Manipulation with R
61 Data Manipulation Overview
62 Guide to Using Dplyr
63 Guide to Using Dplyr – Part 2
64 Pipe Operator
65 Quick note on Dpylr exercise
66 Dplyr Training Exercise
67 Dplyr Training Exercise – Solutions Walkthrough
68 Guide to Using Tidyr

Data Visualization with R
69 Overview of ggplot2
70 ggplot2 Exercise Solutions
71 Histograms
72 Scatterplots
73 Barplots
74 Boxplots
75 Variable Plotting
76 Coordinates and Faceting
77 Themes
78 ggplot2 Exercises

Data Visualization Project
79 Data Visualization Project
80 Data Visualization Project – Solutions Walkthrough – Part 1
81 Data Visualization Project Solutions Walkthrough – Part 2

Interactive Visualizations with Plotly
82 Overview of Plotly and Interactive Visualizations
83 Resources for Plotly and ggplot2

Capstone Data Project
84 Introduction to Capstone Project
85 Capstone Project Solutions Walkthrough

Introduction to Machine Learning with R
86 ISLR PDF
87 Introduction to Machine Learning

Machine Learning with R – Linear Regression
88 Introduction to Linear Regression
89 Linear Regression with R – Part 1
90 Linear Regression with R – Part 2
91 Linear Regression with R – Part 3

Machine Learning Project – Linear Regression
92 Introduction to Linear Regression Project
93 ML – Linear Regression Project – Solutions Part 1
94 ML – Linear Regression Project – Solutions Part 2

Machine Learning with R – Logistic Regression
95 Introduction to Logistic Regression
96 Logistic Regression with R – Part 1
97 Logistic Regression with R – Part 2

Machine Learning Project – Logistic Regression
98 Introduction to Logistic Regression Project
99 Logistic Regression Project Solutions – Part 1
100 Logistic Regression Project Solutions – Part 2
101 Logistic Regression Project – Solutions Part 3

Machine Learning with R – K Nearest Neighbors
102 Introduction to K Nearest Neighbors
103 K Nearest Neighbors with R

Machine Learning Project – K Nearest Neighbors
104 Introduction K Nearest Neighbors Project
105 K Nearest Neighbors Project Solutions

Machine Learning with R – Decision Trees and Random Forests
106 Introduction to Tree Methods
107 Decision Trees and Random Forests with R

Machine Learning Project – Decision Trees and Random Forests
108 Introduction to Decision Trees and Random Forests Project
109 Tree Methods Project Solutions – Part 1
110 Tree Methods Project Solutions – Part 2

Machine Learning with R – Support Vector Machines
111 Introduction to Support Vector Machines
112 Support Vector Machines with R

Machine Learning Project – Support Vector Machines
113 Introduction to SVM Project
114 Support Vector Machines Project – Solutions Part 1
115 Support Vector Machines Project – Solutions Part 2

Machine Learning with R – K-means Clustering
116 Introduction to K-Means Clustering
117 K Means Clustering with R

Machine Learning Project – K-means Clustering
118 Introduction to K Means Clustering Project
119 K Means Clustering Project – Solutions Walkthrough

Machine Learning with R – Natural Language Processing
120 Introduction to Natural Language Processing
121 Natural Language Processing with R – Part 1
122 Natural Language Processing with R – Part 2

Machine Learning with R – Neural Nets
123 Introduction to Neural Nets
124 Neural Nets with R

Machine Learning Project – Neural Nets
125 Introduction to Neural Nets Project
126 Neural Nets Project – Solutions

Bonus Section – Discounts for Other Courses
127 Bonus Lecture Coupons