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Applied Machine Learning in R (Udemy.com)

Get the essential machine learning skills and use them in real life situations

4.6
295 reviews

Created by: Bogdan Anastasiei

Last updated December 2020

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Overall Score : 92 / 100

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Course Description

This course offers you practical training in machine learning, using the R program. At the end of the course you will know how to use the most widespread machine learning techniques to make accurate predictions and get valuable insights from your data. All the machine learning procedures are explained live, in detail, on real life data sets. So you will advance fast and be able to apply your knowledge immediately - " no need for painful trial-and-error to figure out how to implement this or that technique in R. Within a short time you can have a solid expertise in machine learning. Machine learning skills are very valuable if you intent to secure a job like data analyst, data scientist, researcher or even software engineer. So it may be the right time for you to enroll in this course and start building your machine learning competences today! Let-'s see what you are going to learn here. First of all, we are going to discuss some essential concepts that you must absolutely know before performing machine learning. So we-'ll talk about supervised and unsupervised machine learning techniques, about the distinctions between prediction and inference, about the regression and classification models and, above all, about the bias-variance trade-off, a crucial issue in machine learning. Next we-'ll learn about cross-validation. This is an all-important topic, because in machine learning we must be able to test and validate our model on independent data sets (also called first seen data). So we are going to present the advantages and disadvantages of three cross-validations approaches. After the first two introductory sections, we will get to study the supervised machine learning techniques. We-'ll start with the regression techniques, where the response variable is quantitative. And no, we are not going to stick to the classical OLS regression that you probably know already. We will study sophisticated regression techniques like stepwise regression (forward and backward), penalized regression (ridge and lasso) and partial least squares regression. And of course, we-'ll demonstrate all of them in R, using actual data sets. Afterwards we-'ll go to the classification techniques, very useful when we have to predict a categorical variable. Here we-'ll study the logistic regression (classical and lasso), discriminant analysis (linear and quadratic), naAve Bayes technique, K nearest neighbor, support vector machine, decision trees and neural networks. For each technique above, the presentation is structured as follows: * a short, easy to understand theoretical introduction (without complex mathematics) * how to train the predictive model in R * how to test the model to make sure that it does a good prediction job on independent data sets. In the last sections we-'ll study two unsupervised machine learning techniques: principal component analysis and cluster analysis. They are powerful data mining techniques that allow you to detect patterns in your data or variables. For each technique, a number of practical exercises are proposed. By doing these exercises you-'ll actually apply in practice what you have learned. This course is your opportunity to become a machine learning expert in a few weeks only! With my video lectures, you will find it very easy to master the major machine learning techniques. Everything is shown live, step by step, so you can replicate any procedure at any time you need it. So click the - Enroll- button to get instant access to your machine learning course. It will surely provide you with new priceless skills. And, who knows, it could give you a tremendous career boost in the near future. See you inside!Who this course is for:
  • Data analysts
  • Data scientists
  • Researchers
  • Students
Read More All Levels All Levels product length iconTime: 8.2h

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Instructor Details

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My name is Bogdan Anastasiei and I am an assistant professor at the University of Iasi, Romania, Faculty of Economics and Business Administration. I teach Internet marketing and quantitative methods for business. I am also a business consultant. I have run quantitative risk analyses and feasibility studies for various local businesses and been implied in academic projects on risk analysis and marketing analysis. I have also written courses and articles on Internet marketing and online communication techniques. I have 24 years experience in teaching and about 15 years experience in business consulting.

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Reviews

4.6

295 ratings on Udemy

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By Pawan Solanki

yes

By Jose Ramon Villatuya

The course gets you to the meat of the matter fast. Further details can be researched after you've learned the fundamentals. I believe some knowledge of statistical inference will be helpful to allow a more quicker consumption of the topics. This course can also serve as a quick revision for those with some background in ML already.

In my opinion, Bogdan Anastasiei was able to structure the course in a manner that minimizes confusion (and maybe frustration) for the initiated-beginning ML learner.

By Satyan

Very Good!

By Debu Dey

Good content

By Fahad Masood Reda

Very Easy to understand , each step is explained in simple terms

By Matthew Rolley

Good content, well taught :)

By Ananth Subramanian

Well Structured !

By Fridah Nyakundi

It explains ML in its simplest form without losing the content in the details.

By Nick Smith

Good overview, and good discrimination between supervised/unsupervised learning methods.

By Lim Kah Kheng

One-liner : GOOD

By Alexandre VASSILTCHENKO

Very good explanation, i ve already the knowledge but Bodgan explanation makes it clear. Hope he'll do other course in machine learning but with "deeper" technics.

By Shridhar K

Very useful course. Thank you.

Showing 12 of 28 reviews

Applied Machine Learning in R
Visit course on Udemy Write a review
  • Video Duration: icon 8.2h
  • Price: $69.99
  • Provider: icon Udemy
  • Updated: icon December 2020
  • Level: All Levels All Levels
  • Language: icon English
  • Certificate: icon Yes
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