Introduction to Machine Learning in R
4.2 (70 ratings)
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Introduction to Machine Learning in R

Learn how to use modern machine learning techniques to all kinds of realistic problems
4.2 (70 ratings)
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.
709 students enrolled
Created by Holczer Balazs
Last updated 2/2017
English
Current price: $10 Original price: $35 Discount: 71% off
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Includes:
  • 7 hours on-demand video
  • 7 Articles
  • 3 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Understand the basics of neural networks
  • Get a good grasp of machine learning fundamentals
  • Learn the basics of R
  • Learn the basics of machine learning techniques
View Curriculum
Requirements
  • No prior programming knowledge is needed
Description

This course is about the fundamental concepts of machine learning, facusing on neural networks. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example. We may construct algorithms that can have a very very good guess about stock prices movement in the market.

In the first chapter we are going to talk about the basics of the R programming language. Later, we will talk about neural networks in the main, and the theory behind it. The last chapter is about the concrete examples of neural networks.

Who is the target audience?
  • This course is mean for newbies who are familiar with R and looking for some advanced topics. No prior programming knowledge is needed.
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Curriculum For This Course
Expand All 82 Lectures Collapse All 82 Lectures 07:12:28
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Introduction
2 Lectures 04:36
+
R Basics
13 Lectures 45:14


Factors
03:15

Arrays
03:46

Matrixes
03:51

List
02:15

Data frames
03:37

Coercion
02:04

Packages
02:57

If - else
02:27

Loops - repeat, while and for
04:20

Custom functions
03:34

Custom operators
01:50
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Data Visualization
3 Lectures 11:50
Scatter plot
02:29

Bar plot
06:29

Pie chart
02:52
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Machine Learning Basics
1 Lecture 06:57
Machine learning basics
06:57
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Linear Regression
3 Lectures 29:15
Linear regression introduction
09:24

Linear regression with gradient descent
08:41

Linear regression example - house prices
11:10
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Logistic Regression
4 Lectures 29:52
Logistic regression introduction
10:15

Logistic regression introduction II - illustration
04:30

Logistic regression example I - machine malfunction
05:44

Logistic regression example II - credit scoring
09:23
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K-Nearest Neighbor Classifier
4 Lectures 35:49
K-nearest neighbor introduction
11:23

Data normalization
03:40

K-nearest neighbor example I
05:18

K-nearest neighbor example II - iris dataset
15:28
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Naive Bayes
2 Lectures 18:00
Naive Bayes introduction
09:03

Naive Bayes example - iris data
08:57
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Support Vector Machine (SVM)
7 Lectures 45:41
Support vector machine introduction I - linear case
08:50

Support vector machine introduction II - non-linear case
07:18

Support vector machine introduction III - kernels
04:23

Support vector machine example I - linear kernels
09:44

Cross validation
03:29

Support vector machine example II - cross validation
03:03

Support vector machine example III - radial kernels
08:54
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Tree-Based Methods
5 Lectures 27:51
Decision trees introduction
09:03

Decision tree example - marketing campaign
07:03

Pruning and bagging introduction
05:07

Boosting introduction
02:55

Random forests introduction
03:43
3 More Sections
About the Instructor
Holczer Balazs
4.4 Average rating
2,610 Reviews
27,348 Students
21 Courses
Software Engineer

Hi!

My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist and later on I decided to get a master degree in applied mathematics. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Quantitative analysts use these algorithms and numerical techniques on daily basis so in my opinion these topics are definitely worth learning.

Take a look at my website and join my email list if you are interested in these topics!