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2021-01-06 14:27:27
30-Day Money-Back Guarantee
Development Data Science Machine Learning

Complete Machine Learning with R Studio - ML for 2021

Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language - R studio
Rating: 4.4 out of 54.4 (1,199 ratings)
148,671 students
Created by Start-Tech Academy
Last updated 2/2021
English
English [Auto]
30-Day Money-Back Guarantee

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

23 sections • 119 lectures • 12h 51m total length

  • Preview02:35
  • Course Resources
    00:05

  • Preview05:52
  • This is a milestone!
    03:31
  • 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

  • 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

  • Introduction to Machine Learning
    16:03
  • Building a Machine Learning Model
    08:42
  • Quiz: Introduction to Machine Learning
    4 questions

  • 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
  • Preview04:15
  • Preview04:49
  • 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
  • Quiz
    1 question

  • 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
  • Assignment 1: Regression Analysis
    1 question

  • 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

  • The Data and the Data Dictionary
    08:14
  • 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

  • Three Classifiers and the problem statement
    03:17
  • Why can't we use Linear Regression?
    04:32

  • 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

Instructor

Start-Tech Academy
1,700,000+ Enrollments | 4+ Rated | 160+ Countries
Start-Tech Academy
  • 4.4 Instructor Rating
  • 35,483 Reviews
  • 927,314 Students
  • 38 Courses

Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. 
Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. 

Founded by Abhishek Bansal and Pukhraj Parikh.

Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in  MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.

Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.


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