Data Science: Machine Learning and Statistical Modeling in R
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Data Science: Machine Learning and Statistical Modeling in R

Master machine learning techniques with R to solve Real-World problems and gain valuable insights from your data.
5.0 (1 rating)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
8 students enrolled
Last updated 8/2017
English
Current price: $10 Original price: $195 Discount: 95% off
5 hours left at this price!
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Includes:
  • 10 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Master prediction and model assessment
  • Work with R, the language of data science
  • Gain deep insights into the application of machine learning tools in the industry
  • Understand and apply machine learning methods using an extensive set of R packages
  • Implement advanced concepts in machine learning
  • Understand how working with complex data is different to standard numerical work
  • Manipulate data in R efficiently to prepare it for analysis
  • Master the skill of recognizing techniques for effective visualization of data
  • Understand why and how to create test and training data sets for analysis
View Curriculum
Requirements
  • No prerequisites, knowledge of some undergraduate level mathematics would be an added advantage
Description

In this course, we will teach you advanced techniques in machine learning with the latest code in R. Now is the time to take control of your data and start producing superior statistical analysis with R. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning and more.

This course starts with teaching you how to set up the R environment, which includes installing RStudio and R packages. This course aims to excite you with awesome projects focused on analysis, visualization, and machine learning. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, and more. We’ll start off with data analysis – this will show you ways to use R to generate professional analysis reports. We’ll then move on to visualizing our data – this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we’ll move into the world of machine learning – this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction.

This course supplies in-depth content that put the theory into practice. You know you need to upgrade your skills to stay relevant. Don’t wait. Enroll in this course today.

Who is the target audience?
  • The course is intended for both students and professionals. Specifically anyone with none or minimal prior experience with programming.
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Curriculum For This Course
106 Lectures
10:02:34
+
Welcome
5 Lectures 33:05

Introduction to R
00:22

Downloading and installing R
06:16

Setting up the environment
18:43

Installing and loading packages
04:41
+
Basic Building Blocks
6 Lectures 39:51
Introduction
00:21

Using R as a calculator
03:36

R variables
04:04

Understanding the different data types
11:25

Storing data in vectors
16:29

Call functions
03:56
+
Advanced Data Structures
5 Lectures 38:08
Introduction
00:18

Data Structures - Data.frames
17:13


Data Structures - Matrices
07:54

Data Structures - Arrays
01:53
+
Reading Data into R
8 Lectures 22:46
Introduction
00:17

Read a CSV File into R
05:51

Excel is not easily readable into R
01:01

Read from database
05:52

Read data files from other statistical tools
01:10

Load binary R files
04:33

Load data included with R
01:41

Scrape data from the web
02:21
+
Creating Statistical Graphs
13 Lectures 37:17
Introduction
00:22

Find the diamonds data
01:06

Make histograms with base graphics
01:23

Make scatterplots with base graphics
01:54

Make boxplots with base graphics
01:32

Get familiar with ggplot2
02:23

Plot histograms and densities
03:45

Make scatterplots
05:05

Make boxplots and violin plots
04:17

Make line plots
08:14

Create small multiples
03:54

Control colors and shapes
01:11

Add themes to graphs
02:11
+
R Programming
13 Lectures 50:18
Introduction
00:21

Hello, World
01:57

The basics of function arguments
10:25

Return a value from a function
02:40

Gain flexibility with do.call
03:39

Use if statements to control program flow
02:01

Stagger if statements with else
05:26

Check multiple statements with switch
03:44

Run checks on entire vectors
05:10

Check compound statements
05:33

Iterate with a for loop
06:00

Iterate with a while loop
01:24

Control loops with break and next
01:58
+
Data Munging
9 Lectures 49:33
Introduction
00:24

Repeat an operation on a matrix using apply
04:38

Repeat an operation on a list
02:58

The mapply
04:26

The aggregate function
05:19

plyr package
17:11

Combine datasets
03:44

Join datasets
05:49

Switch storage paradigms
05:04
+
Learn How to Manipulate Strings
3 Lectures 39:27
Introduction
00:13

Combine strings together
07:21

Extract text
31:53
+
Statistics in R
4 Lectures 56:12
Introduction
00:13

Draw numbers from probability distributions
21:03

Calculate averages, standard deviations and correlations
16:05

Compare samples with t-tests and analysis of variance
18:51
+
Learn Linear Models
12 Lectures 01:37:00
Introduction
00:19

Fit simple linear models
10:07

Explore the data
08:26

Fit multiple regression models
19:09

Fit logistic regression
09:59

Fit Poisson regression
06:58

Analyze survival data
11:54

Assess model quality with residuals
05:08

Compare models
07:11

Judge accuracy
08:59

Estimate uncertainty with the bootstrap
06:16

Choose variables using stepwise selection
02:34
6 More Sections
About the Instructor
Star Academy, Inc.
5.0 Average rating
1 Review
8 Students
1 Course
High Quality Courses from Expert Instructors

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