Machine Learning Made Easy : Beginner to Advance using R
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Machine Learning Made Easy : Beginner to Advance using R

Learn Machine Learning Algorithms using R from experts with hands on examples and practice sessions. With 5 different pr
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0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
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Last updated 8/2017
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Current price: $12 Original price: $185 Discount: 94% off
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Includes:
  • 15.5 hours on-demand video
  • 23 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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What Will I 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.
View Curriculum
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 is the target audience?
  • 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.
Compare to Other Data Science Courses
Curriculum For This Course
129 Lectures
15:16:14
+
Introduction to R
10 Lectures 01:18:52


R Packages
05:11

R Data types Vectors
12:48

R Data Frames
13:17

List
10:39

Factor and Matrix
04:12

R History and Scripts
09:43

R Functions
05:06

Errors
07:29
+
Data Handling in R
9 Lectures 47:56
Introduction to Data Handling
01:30

Importing the Datasets
06:00

Checklist
05:14

Subsetting the Data
04:41

Subsetting Variable Condition
05:38

Calculated Fields_ifelse
07:27

Sorting and Duplicates
09:02

Joining and Merging
05:22

Exporting the Data
03:02
+
Basic Statistics and Graph
5 Lectures 29:01
Introduction and Sampling
03:35

Descriptive Statistics
09:09

Percentiles and Quartiles
04:23

Box Plots
04:53

Creating Graphs and Conclusions
07:01
+
Data Cleaning and Treatment
14 Lectures 01:39:36
Introduction to Data Cleaning and Model Building Cycle
02:17

Model Building Cycle
07:19

Data Cleaning Case Study
06:59

CS lab step one basic content of dataset
10:01

Variable Level Exploration Catagorical
04:35

Reading Data Dictionary
10:19

Step two Lab Catagorical Variable Exploration
12:47

Step three Lab Variable Level Exploration Continues
11:00

Data Cleaning and Treatment
07:45

Step four Treatment-Scenario 1
06:38

Step four Treatment-Scenario 2
09:25

Data Cleaning Scenario 3
05:31

Some Other Variables
02:40

Conclusions
02:20
+
Linear Regression
11 Lectures 01:22:28
Introduction and Corelation
04:19

LBA Corelation Calculation in R
04:35

Beyond Pearson Corelation
03:09

From Corelation to Regression
09:26

Regression Line Fitting in R
07:59

R Squared
11:54

Multiple Regression
09:40

Adjusted R Squared
04:50

Issue with Multiple Regression
11:45

Multicollinearity
12:42

Regression Conclusion
02:09
+
Logistic Regression
8 Lectures 01:08:01
Need of Non-Linear Regression
14:05

Logistic Function and Line
09:14

Multiple Logistic Regression
07:25

Goodness of Fit for a Logistic Regression
11:43

Multicollinearity in Logistic Regression
07:18

Individual Impact of Variables
04:53

Model Selection
12:00

Logistic Regression Conclusion
01:23
+
Decision Tree
13 Lectures 02:13:58
Introduction to Decision Tree and Segmentation
06:31

The Decision Tree Philosophy & The Decision Tree Approach
14:14

The Splitting Criterion & Entropy Calculation
15:24

Information Gain & Calculation
08:56

The Decision Tree Algorithm
10:35

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

Pruning & Complexity Parameters
05:33

Choosing Cp & Cross Validation Error
10:44

Two Types of Pruning
03:00

Tree Building and Model Selection
14:20

Conclusion
02:04
+
MSCV
12 Lectures 01:30:12
Introduction to Model Selection
02:00

Sensitivity Specificity
09:20

Sensitivity Specificity Continued
09:30

ROC AUC
09:57

The Best Model
04:11

Errors
05:50

Overfitting Underfitting
13:01

Bias_Variance Treadoff
09:53

Holdout Data Validation
05:08

Ten fold CV
10:39

Kfold CV
09:00

MSCV Conclusion
01:43
+
Neural Networks
13 Lectures 01:22:40
Introduction and Logistic Regression Recap
06:45

Decision Boundary
03:01

Non Linear Decision Boundary NN
06:42

Non Linear Decision Boundary and Solution
10:45

Neural Net Intution
07:35

Neural Net Algorithm
06:47

Neural Net Algorithm Demo
06:16

Building a Neural Network
10:17

Local Vs Global Min
05:09

Digit Recognizer second attempt part1
03:35

Digit Recognizer second attempt part2
06:02

Lab Digit Reconizer
04:10

Conclusion
05:36
+
SVM
12 Lectures 50:48
Introduction to SVM
02:01

The Classifier and Decision Boundary
05:29

SVM- The Large Margin Classifier
01:34

The SVM Alogirithm and Results
03:55

SVM on R
04:46

Non Linear Boundary
03:38

Kernal Trick
05:52

Kernal Trick on R
06:45

Soft Margin and Validation
03:46

SVM Advantage, Disadvantage and Applications
02:56

Lab Digit Reconizer
08:58

SVM Conclusion
01:08
2 More Sections
About the Instructor
Statinfer Solutions
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1 Course
Data Science starts here!

Statinfer is the data science e-learning solutions provider. We provide online and class room training on leading data science tools and techniques.

Our focus is on data analytics, machine learning, and big data. The tools that we work on are R, Python, Hadoop, and Spark.

Statinfer is created by data scientists who understand the dynamics of the current business.

Our courses are not merely academic, instead, there are many industrial applications and examples. The creators assembled the course, well studied the topics with a clear understanding and had designed the curriculum.

Each course has ample amount of self-practising labs, quizzes and projects on real data to get an exposure to real world problems.