Machine Learning Basics: Classification models in R
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- Certificate on Completion
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You're looking for a complete Classification modelling course that teaches you everything you need to create a Classification model in R, right?
You've found the right Classification modelling course!
After completing this course, you will be able to:
· Identify the business problem which can be solved using Classification modelling techniques of Machine Learning.
- Create different Classification modelling model in R and compare their performance.
- Confidently practice, discuss and understand Machine Learning concepts
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 Classification techniques of machine learning, such as Logistic Regression, Linear Discriminant Analysis and KNN
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem using classification techniques.
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
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.
What is covered in this course?
This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.
Below are the course contents of this course on Linear Regression:
- Section 1 - Basics of Statistics
This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation
- Section 2 - R basic
This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R.
- Section 3 - Introduction to Machine Learning
In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.
- Section 4 - Data Pre-processing
In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.
We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation.
- Section 5 - Classification Models
This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors.
We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem.
By the end of this course, your confidence in creating a classification model in R will soar. You'll have a thorough understanding of how to use Classification modelling to create predictive models and solve business problems.
Go ahead and click the enroll button, and I'll see you in lesson 1!
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.
Which all classification techniques are taught in this course?
In this course we learn both parametric and non-parametric classification techniques. The primary focus will be on the following three techniques:
- Logistic Regression
- Linear Discriminant Analysis
- K - Nearest Neighbors (KNN)
How much time does it take to learn Classification techniques of machine learning?
Classification is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn classification starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of classification.
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
- 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.
- 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.
- 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.
- 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.
- 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
- Students will need to install R and R studio software but we have a separate lecture to help you install the same
- Understand how to interpret the result of Logistic Regression model and translate them into actionable insight
- Learn the linear discriminant analysis and K-Nearest Neighbors technique
- Learn how to solve real life problem using the different classification techniques
- Preliminary analysis of data using Univariate analysis before running classification model
- Predict future outcomes basis past data by implementing Machine Learning algorithm
- Indepth knowledge of data collection and data preprocessing for Machine Learning logistic regression problem
- Course contains a end-to-end DIY project to implement your learnings from the lectures
- Graphically representing data in R before and after analysis
- How to do basic statistical operations in R
Welcome to the course!
Introduction to Machine Learning
Building a Machine Learning model
Types of Data
Types of Statistics
Describing data Graphically
Measures of Centers
Measures of Dispersion
Installing R and R studio
Basics of R and R studio
Packages in R
Inputting data part 1: Inbuilt datasets of R
Inputting data part 2: Manual data entry
Inputting data part 3: Importing from CSV or Text files
Creating Barplots in R
Creating Histograms in R
Gathering Business Knowledge
The Data and the Data Dictionary
Importing the dataset into R
Univariate analysis and EDD
EDD in R
Outlier Treatment in R
Missing Value Imputation
Missing Value imputation in R
Seasonality in Data
Variable transformation in R
Dummy variable creation: Handling qualitative data
Dummy variable creation in R
Three Classifiers and the problem statement
Why can't we use Linear Regression?
Training a Simple Logistic model in R
Result of Simple Logistic Regression
Logistic with multiple predictors
Training multiple predictor Logistic model in R
Evaluating Model performance
Predicting probabilities, assigning classes and making Confusion Matrix
Linear Discriminant Analysis
Linear Discriminant Analysis in R
Test-Train Split in R
K-Nearest Neighbors classifier
K-Nearest Neighbors in R
Understanding the results of classification models
Summary of the three models
From 0 to 1: Hive for Processing Big DataLoony Corn
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