Machine Learning Made Easy : Beginner to Advanced using R
4.2 (63 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4,220 students enrolled

Machine Learning Made Easy : Beginner to Advanced using R

Learn Machine Learning Algorithms using R from experts with hands on examples and practice sessions. With 5 different pr
4.2 (63 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4,220 students enrolled
Last updated 4/2018
English
English
Current price: $13.99 Original price: $19.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 15.5 hours on-demand video
  • 36 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • R Programming, Data Handling and Cleaning, Basic Statistics, Classical Machine Learning Algorithms, Model Selection and Validation, Advanced Machine Learning Algorithms, Ensemble Learning.
  • Write your own R scripts and work in R environment.
  • Import, manipulate, clean up, sanitize and export datasets.
  • Understand basic statistics and implement using R.
  • Understand data science life cycle while understanding steps of building, validating, improving and implementing the machine learning models.
  • Do powerful analysis on data, find insights and present them in visual manner.
  • Learn classical algorithms like Linear Regression, Logistic Regression, Decision Trees and advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.
  • Know how each machine learning algorithm works and which one to choose according to the type of problem.
  • Build more than one powerful machine learning model and be able to select the best one and improve it further.
Course content
Expand all 129 lectures 15:16:14
+ Introduction to R
10 lectures 01:18:52
R Packages
05:11
R Data types Vectors
12:48
List
10:39
R History and Scripts
09:43
R Functions
05:06
Errors
07:29
Introduction to R quiz
15 questions
+ Data Handling in R
9 lectures 47:56
Checklist
05:14
Subsetting the Data
04:41
Subsetting Variable Condition
05:38
Calculated Fields_ifelse
07:27
Joining and Merging
05:22
Exporting the Data
03:02
Data handling quiz
8 questions
+ Basic Statistics and Graph
5 lectures 29:01
Introduction and Sampling
03:35
Descriptive Statistics
09:09
Percentiles and Quartiles
04:23
Creating Graphs and Conclusions
07:01
Basic Statistics and graph quiz
14 questions
+ Data Cleaning and Treatment
14 lectures 01:39:36
CS lab step one basic content of dataset
10:01
Variable Level Exploration Catagorical
04:35
Reading Data Dictionary
10:19
Step three Lab Variable Level Exploration Continues
11:00
Step four Treatment-Scenario 1
06:38
Step four Treatment-Scenario 2
09:25
Some Other Variables
02:40
Conclusions
02:20
+ Linear Regression
11 lectures 01:22:28
LBA Correlation Calculation in R
04:35
Beyond Pearson Correlation
03:09
Regression Line Fitting in R
07:59
Multiple Regression
09:40
Adjusted R Squared
04:50
Regression Conclusion
02:09
Regression Quiz
15 questions
+ Logistic Regression
8 lectures 01:08:01
Multiple Logistic Regression
07:25
Multicollinearity in Logistic Regression
07:18
Individual Impact of Variables
04:53
Logistic Regression Conclusion
01:23
Logistic Regression Quiz
9 questions
+ Decision Tree
13 lectures 02:13:58
The Splitting Criterion & Entropy Calculation
15:24
Information Gain & Calculation
08:56
Split for Variable & The Decision Tree Lab - Part 1
14:24
The Decision Tree Lab - Part 2 & Validation
12:04
The Decision Tree Lab - Part 3 & Overfitting
16:09
Conclusion
02:04
Decision Trees Quiz
11 questions
+ Model Selection and Cross Validation
12 lectures 01:30:12
Sensitivity Specificity
09:20
ROC AUC
09:57
Errors
05:50
Bias_Variance Treadoff
09:53
Ten fold CV
10:39
Kfold CV
09:00
Model selection cross validation Quiz
15 questions
+ Neural Networks
13 lectures 01:22:40
Introduction and Logistic Regression Recap
06:45
Non Linear Decision Boundary and Solution
10:45
Neural Net Algorithm
06:47
Building a Neural Network
10:17
Local Vs Global Min
05:09
Digit Recognizer second attempt part2
06:02
Lab Digit Reconizer
04:10
Neural Networks
8 questions
+ Support Vector Machines
12 lectures 50:48
Introduction to SVM
02:01
SVM- The Large Margin Classifier
01:34
The SVM Alogirithm and Results
03:55
Non Linear Boundary
03:38
Soft Margin and Validation
03:46
SVM Advantage, Disadvantage and Applications
02:56
SVM Conclusion
01:08
support vector machine
7 questions
Requirements
  • Familiarity with high school mathematics.
Description

Want to know how Machine Learning algorithms work and how people apply it to solve data science problems? You are looking at right course!

This course has been created, designed and assembled by professional Data Scientists who have worked in this field for nearly a decade. We can help you understand the complex machine learning algorithms while keeping you grounded to the implementation on real business and data science problems.

We will let you feel the water and coach you to become a full swimmer in the realm of data science and Machine Learning. Every tutorial will increase your skill level by challenging your ability to foresee, yet letting you improve upon self.

We are sure that you will have fun while learning from our tried and tested structure of course to keep you interested in what’s coming next.

Here is how the course is going to work:

  • Part 1 – Introduction to R Programming.
    • This is the part where you will learn basic of R programming and familiarize yourself with R environment.
    • Be able to import, export, explore, clean and prepare the data for advance modeling.
    • Understand the underlying statistics of data and how to report/document the insights.
  • Part 2 – Machine Learning using R
    • Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees.
    • Learn which algorithm to choose for specific problem, build multiple model, learn how to choose the best model and be able to improve upon it.
    • Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.

Features:

  • Fully packed with LAB Sessions. One to learn from and one for you to do it yourself.
  • Course includes R code, Datasets and other supporting material at the beginning of each section for you to download and use on your own.
  • Quiz after each section to test your learning.

Bonus:

  • This course is packed with 5 projects on real data related to different domains to prepare you for wide variety of business problems.
  • These projects will serve as your step by step guide to solve different business and data science problems.

Who this course is for:
  • Anyone interested in Data Science and Machine Learning.
  • Students who want a head start in Data Science field.
  • Data analysts who want to upgrade their skills in Machine Learning.
  • People who want to add value to their work and business by using Machine Learning.
  • People with basics understanding of classical machine learning algorithms like linear regression or logistic regression, but want to learn more about it.
  • People interested in understanding application of machine learning algorithms on real business problems.
  • People interested in understanding how a machine learning algorithm works and what's the math behind it.