Complete Machine Learning with R Studio - ML for 2020
4.3 (650 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.
81,678 students enrolled

Complete Machine Learning with R Studio - ML for 2020

Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language - R studio
4.3 (650 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.
81,678 students enrolled
Last updated 7/2020
English
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 13 hours on-demand video
  • 6 articles
  • 6 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
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What you'll learn
  • Learn how to solve real life problem using the Machine learning techniques
  • Machine Learning models such as Linear Regression, Logistic Regression, KNN etc.
  • Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc.
  • Understanding of basics of statistics and concepts of Machine Learning
  • How to do basic statistical operations and run ML models in R
  • Indepth knowledge of data collection and data preprocessing for Machine Learning problem
  • How to convert business problem into a Machine learning problem
Course content
Expand all 119 lectures 12:46:35
+ Welcome to the course
2 lectures 02:45
Course resources: Notes and Datasets (Part 1)
00:10
+ Setting up R Studio and R crash course
8 lectures 01:01:36
Basics of R and R studio
10:47
Packages in R
10:52
Inputting data part 1: Inbuilt datasets of R
04:21
Inputting data part 2: Manual data entry
03:11
Inputting data part 3: Importing from CSV or Text files
06:49
Creating Barplots in R
13:43
Creating Histograms in R
06:01
+ Basics of Statistics
5 lectures 30:08
Types of Data
04:04
Types of Statistics
02:45
Describing the data graphically
11:37
Measures of Centers
07:05
Measures of Dispersion
04:37
+ Intorduction to Machine Learning
2 lectures 24:45
Introduction to Machine Learning
16:03
Building a Machine Learning Model
08:42

Answer the Questions basis the concepts learnt in previous two lectures

Quiz: Introduction to Machine Learning
4 questions
+ Data Preprocessing for Regression Analysis
18 lectures 01:52:17
Gathering Business Knowledge
03:26
Data Exploration
03:19
The Data and the Data Dictionary
07:31
Importing the dataset into R
03:00
Univariate Analysis and EDD
03:34
EDD in R
12:43
Missing Value imputation
03:36
Missing Value imputation in R
03:49
Seasonality in Data
03:35
Bi-variate Analysis and Variable Transformation
16:14
Variable transformation in R
09:37
Non Usable Variables
04:44
Dummy variable creation: Handling qualitative data
04:50
Dummy variable creation in R
05:01
Correlation Matrix and cause-effect relationship
10:05
Correlation Matrix in R
08:09
+ Linear Regression Model
12 lectures 01:29:47
The problem statement
01:25
Basic equations and Ordinary Least Squared (OLS) method
08:13
Assessing Accuracy of predicted coefficients
14:40
Assessing Model Accuracy - RSE and R squared
07:19
Simple Linear Regression in R
07:40
Multiple Linear Regression
04:57
The F - statistic
08:22
Interpreting result for categorical Variable
05:04
Multiple Linear Regression in R
07:50
Quiz
1 question
Test-Train split
09:32
Bias Variance trade-off
06:01
Test-Train Split in R
08:44
Determine an equation that can predict the output variable. Identify which all variables impact the response variable.
Assignment 1: Regression Analysis
1 question
+ Regression models other than OLS
5 lectures 43:36
Linear models other than OLS
04:18
Subset Selection techniques
11:34
Subset selection in R
07:38
Shrinkage methods - Ridge Regression and The Lasso
07:14
Ridge regression and Lasso in R
12:52
+ Classification Models: Data Preparation
8 lectures 43:07
The Data and the Data Dictionary
08:14
Course resources: Notes and Datasets
00:02
Importing the dataset into R
03:00
EDD in R
11:26
Outlier Treatment in R
04:49
Missing Value imputation in R
03:49
Variable transformation in R
06:28
Dummy variable creation in R
05:19
+ The Three classification models
2 lectures 07:49
Three Classifiers and the problem statement
03:17
Why can't we use Linear Regression?
04:32
+ Logistic Regression
8 lectures 38:39
Logistic Regression
07:54
Training a Simple Logistic model in R
03:34
Results of Simple Logistic Regression
05:11
Logistic with multiple predictors
02:22
Training multiple predictor Logistic model in R
01:48
Confusion Matrix
03:47
Evaluating Model performance
07:40
Predicting probabilities, assigning classes and making Confusion Matrix in R
06:23
Quiz
1 question
Requirements
  • Students will need to install R and R studio software but we have a separate lecture to help you install the same
Description

You're looking for a complete Machine Learning course that can help you launch a flourishing career in the field of Data Science & Machine Learning, right?

You've found the right Machine Learning course!

After completing this course you will be able to:

· Confidently build predictive Machine Learning models to solve business problems and create business strategy

· Answer Machine Learning related interview questions

· Participate and perform in online Data Analytics competitions such as Kaggle competitions


Check out the table of contents below to see what all Machine Learning models you are going to learn.


How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning.

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through linear regression.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.



Below is a list of popular FAQs of students who want to start their Machine learning journey-

What is Machine Learning?

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

What are the steps I should follow to be able to build a Machine Learning model?

You can divide your learning process into 3 parts:

Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.

Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model

Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python

Understanding of  models - Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.

Why use R for Machine Learning?

Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R

1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.

2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.

3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.

4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.

5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.

Who this course is for:
  • People pursuing a career in data science
  • Working Professionals beginning their Data journey
  • Statisticians needing more practical experience