Introduction to Machine Learning in R

Learn how to use modern machine learning techniques to all kinds of realistic problems
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Instructed by Holczer Balazs IT & Software / Other
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  • Lectures 60
  • Length 5.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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About This Course

Published 9/2015 English

Course 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.

What are the requirements?

  • No prior programming knowledge is needed

What am I going to get from this course?

  • 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

What 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.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction
Introduction
Preview
01:01
Environment
Preview
01:58
Section 2: R Basics
First steps
Preview
04:56
Vectors
Preview
04:17
Factors
03:23
Arrays
03:28
Matrixes
03:53
List
02:39
Data frames
04:02
Coercion
02:11
Packages
04:25
If - else
02:09
Loops - repeat, while and for
05:09
Custom functions
04:29
Custom operators
01:51
Section 3: Data Visualization
Scatter plot
02:54
Bar plot
06:29
Pie chart
02:52
Section 4: Machine Learning Basics
Machine learning basics
05:47
Section 5: Linear Regression
Linear regression introduction
06:51
Linear regression example - house prices
11:10
Section 6: Logistic Regression
Logistic regression introduction
12:59
Logistic regression example I - machine malfunction
05:44
Logistic regression example II - credit scoring
09:23
Section 7: K-Nearest Neighbour Classifier
K-nearest neighbour introduction
09:13
Data normalization
02:35
K-nearest neighbour example I
05:18
K-nearest neighbour example II
15:28
Section 8: Naive Bayes
Naive Bayes introduction
07:50
Naive Bayes example - iris data
08:57
Section 9: Support Vector Machine (SVM)
Support vector machine introduction
15:07
Support vector machine example I - linear kernels
09:44
Cross validation
04:05
Support vector machine example II - cross validation
03:03
Support vector machine example III - radial kernels
08:54
Section 10: Tree-Based Methods
Decision trees introduction
09:36
Decision tree example - marketing campaign
07:03
Pruning and bagging introduction
03:32
Random forests introduction
02:17
Boosting introduction
02:47
Section 11: Clustering
Principal component analysis introduction
02:50
K-means clustering introduction
07:43
K-means clustering example
04:26
DBSCAN introduction
05:56
Hierarchical clustering introduction
05:54
Hierarchical clustering example
03:59
Section 12: Neural Networks
Introduction to neural networks
11:03
Feedfordward neural networks
07:42
The training method
08:36
Error calculation
03:12
Gradients calculation
08:35
Backpropagation
03:18
Applications of neural networks
05:56
Deep learning
02:41
Neural network example I - learning square numbers
06:27
Neural network example II - XOR problem
07:30
Neural network example III - credit scoring
09:41
Section 13: Data
Data
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Slides
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Instructor Biography

Holczer Balazs, 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.

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