Introduction to Machine Learning in R (Udemy.com)

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

( 40 Reviews )

Created by: Holczer Balazs

Produced in 2021

What you will 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

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 78 / 100

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 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
boosting

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.

Time: 8.5h

Instructor Details

Holczer Balazs

3.9 Rating
40 Reviews

Hi!
My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. 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.
Take a look at my website if you are interested in these topics!