Introduction to Machine Learning in R

Introduction to Machine Learning in R

Machine learning, neural networks, regression, SVM, naive bayes classifier, bagging, boosting, random forest classifier

What you’ll 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

Introduction to Machine Learning in R


No prior programming knowledge is needed


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 good guess about stock prices movement in the market.

Section 1:

R basics

data visualization

machine learning basics

Section 2:

linear regression and implementation

Section 3:

logistic regression and implementation

Section 4:

k-nearest neighbor classifier and implementation

Section 5:

naive bayes classifier and implementation

support vector machines (SVMs)

Section 6:

tree based approaches

decision trees

random forest classifier

Section 7:

clustering algorithms

k means clustering and hierarchical clustering


Section 8:

neural networks in R

feedforward neural networks and its applications

credit scoring with neural networks

Thanks for joining the course, let’s get started!

Who this course is for:

This course is mean for newbies who are familiar with R and looking for some advanced topics. No prior programming knowledge is needed.

Course information :

h264, yuv420p, 1280×720 |ENGLISH, aac, 48000 Hz, 2 channels, s16 | 8h 32 mn | 1.12 GB
Instructor: Holczer Balazs

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