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30-Day Money-Back Guarantee

This course includes:

  • 8 hours on-demand video
  • 4 articles
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Machine Learning

Applied Machine Learning in R

Get the essential machine learning skills and use them in real life situations
Rating: 4.4 out of 54.4 (174 ratings)
13,770 students
Created by Bogdan Anastasiei
Last updated 12/2020
English
30-Day Money-Back Guarantee

What you'll learn

  • Understand the essential concepts related to machine learning
  • Perform model cross-validation to assess model stability on independent data sets
  • Execute advanced regression analysis techniques: best subset selection regression, penalized regression, PLS regression
  • Perform logistic regression and discriminant analysis
  • Apply complex classification techniques: naive Bayes, K nearest neighbor, support vector machine, decision trees
  • Use neural networks to make predictions
  • Use principal components analysis to detect patterns in variables
  • Conduct cluster analysis to group observations into homogeneous classes

Course content

18 sections • 80 lectures • 8h 10m total length

  • Preview04:05

  • Preview03:52
  • Preview02:59
  • Preview04:21
  • Preview03:32
  • Computing Prediction Accuracy of Regression Models
    05:06
  • Computing Prediction Accuracy of Classification Models
    07:56
  • Bias-Variance Tradeoff
    07:09

  • What Is Cross-Validation?
    01:20
  • Validation Set Approach
    03:05
  • Leave-One-Out Cross-Validation Approach
    03:35
  • K-Fold Cross-Validation Approach
    03:24

  • Introduction to the OLS Regression
    07:42
  • Validating the OLS Regression Model (1)
    06:26
  • Validating the OLS Regression Model (2)
    05:55

  • Best Subset Selection Regression - Introduction
    16:41
  • Forward Selection Regression
    06:42
  • Backward Selection Regression
    04:28
  • Validating the Subset Selection Regression
    12:57

  • Ridge Regression
    09:58
  • Validating the Ridge Regression
    06:07
  • Lasso Regression
    07:19
  • Validating the Lasso Regression
    05:07

  • Introduction to PLS Regression
    10:04
  • Validating the PLS Regression
    03:06

  • Introduction to Logistic Regression
    14:37
  • Computing the Prediction Accuracy
    05:43
  • Building the ROC Curve
    06:00
  • Validating the Logistic Regression
    06:05
  • Lasso Logistic Regression
    06:36
  • Validating the Lasso Logistic Regression
    09:14

  • Linear Discriminant Analysis
    09:21
  • Validating the Linear Discriminant
    02:32
  • Quadratic Discriminant Analysis
    03:23
  • Validating the Quadratic Discriminant
    02:21

  • Introduction to Naive Bayes Estimation
    11:32
  • Naive Bayes Estimation in R with the e1071 Package
    11:08
  • Validating the Naive Bayes Model
    02:53
  • Naive Bayes Estimation in R with the naivebayes Package
    04:45

Requirements

  • Knowledge of the R program
  • Basic knowledge of statistics and statistical analysis

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), naïve 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

Instructor

Bogdan Anastasiei
University Teacher and Consultant
Bogdan Anastasiei
  • 4.4 Instructor Rating
  • 5,434 Reviews
  • 195,699 Students
  • 12 Courses

      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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