Machine Learning Course with R

Learn in-depth concepts of Machine Learning from the experts

  • 40 hours of Instructor led Training
  • Comprehensive Hands-on with R programming
  • Covers Supervised and Unsupervised learning algorithms
  • Covers Ensemble Machine Learning
Group Discount

Description

Alexa Echo, Amazon’s innovative drone Prime Air, and their amazing retail experience Amazon Go are just the tip of the iceberg. Machine learning is on the brink of new adventures, and is among the most sought after sectors for intelligent professionals today. Based on the premise that systems can sort out information from data, identify patterns and make informed decisions without explicit human intervention, machine learning is poised to reinvent our lives as we know it.

KnowledgeHut brings you a comprehensive course that will help you go from basic to advanced concepts in Machine Learning using R, the language that was built by statisticians, for statisticians. Learn to build systems that learn from experience, and exploit data to create simple predictive models of the world. Machine Learning with R looks into Supervised vs Unsupervised Learning, the ways in which Statistical Modeling relates to Machine Learning, and carries out a comparison of each using R libraries. You will master not only the theory, but also see how it is applied in industry by learning to build predictive models using Machine Learning techniques.

Machine Learning is immensely exciting and creative, and those who have a deep understanding of this smart technology are well equipped to embark on one of the most lucrative careers of this age. Get started on creating innovation that is powered by new-age thinking; become a part of the Machine Learning revolution today!

What You Will Learn

Prerequisites
  • Elementary programming knowledge 
  • Familiarity with statistics

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who should Attend?

  • Those interested in learning ML algorithms for real life business problems
  • Software or Data Engineers interested in learning quantitative analysis and ML

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objectives:

In this module, you will visit the basics of statistics like mean (expected value), median and mode. You will understand the distribution of data in terms of variance, standard deviation and interquartile range; and explore data and measures and simple graphics analyses.Through daily life examples, you will understand the basics of probability. Going further, you will learn about marginal probability and its importance with respect to data science. You will also get a grasp on Baye's theorem and conditional probability and learn about alternate and null hypotheses.

Topics Covered:

  • Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores

Hands-on:

  • Measures of Central Tendency
  • Visualize & Test various distributions
  • Summary Statistics
  • Calculate Probabilities for real life situations
  • Exercises of Hypothesis Testing in real scenario

Learning Objectives:

In this module, you will get an introduction to R, and understand why it is so popular among Data Scientists. Starting with the installation of R and its components, you will load and learn about frequently used libraries. This module touches upon data structures in R, loops and control statements in R and teaches you how to write custom functions, nested functions and functions with arguments.You will learn all about loop functions available in R which are efficient and can be written with a single command.

Going further, you will explore string manipulations and regular expressions and see how functions can be extremely useful for text or unstructured data manipulations. This module also teaches how to import data from various sources in R and also how to write files from R and connect to various databases from R. You will get an overview of visualization in R with base and ggplot libraries, and grasp the Grammarof Graphics in a very structured easy-to-understand manner. The module ends with a hands-on session on a real-life case study.

Topics Covered:

  • Intro to R Programming
  • Installing and Loading Libraries
  • Data Structures in R
  • Control & Loop Statements in R
  • Functions in R
  • Loop Functions in R
  • String Manipulation & Regular Expression in R
  • Working with Data in R
  • Data Visualization in R
  • Case Study

Hands-on: 

  • Installation of R, Libraries. Loading Libraries. Troubleshooting
  • Handle various types of data structures in R
  • Write control statements in R
  • Write custom functions, call & pass arguments to the functions
  • Use in-built loop functions in R
  • Use inbuilt R functions for strong manipulations & regular expressions
  • Complex Real-Life Data Manipulation, Preparation & Exploratory Data Analysis case study

Learning Objectives:

This module will take you through real-life examples of Machine Learning and how it affects society in multiple ways. You can explore many algorithms and models like Classification, Regression, and Clustering. You will also learn about Supervised vs Unsupervised Learning, and look into how Statistical Modeling relates to Machine Learning.

Topics Covered:

  • Machine Learning Modelling Flow
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off
  • Overfitting & Underfitting
  • How to treat Data in ML

Hands-on: 

  • Hands-on Data Manipulation
  • Hands-on evaluation with performance measures of models

Learning Objectives: 

This module gives you an understanding of various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.

Topics Covered:

  • Maxima and Minima
  • Cost Function
  • Learning Rate
  • Optimization Techniques

Hands-on :
Learn about cost-functions using R code.

Learning Objectives: 

In this module you will learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies. It covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance.You will be able to grasp the concepts of Linear and Logistic Regression with real-life case studies. Through a case study on KNN Classification, you will learn how KNN can be used for a classification problem. You will further explore Naive Bayesian Classifiers through another case study, and also understand how Support Vector Machines can be used for a classification problem. The module also covers hyper-parameter tuning like regularization and a case study on SVM.

Topics Covered:

  • Linear Regression
  • Case Study
  • Logistic Regression
  • Case Study
  • K-NN Classification
  • Case Study
  • Naive Bayesian classifiers
  • Case Study
  • SVM - Support Vector Machines
  • Case Study

Hands-on:

  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.
  • This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • We receive 100s of emails & text messages everyday. Many of them are spams. We would like to classify our spam messages and send them to the spam folder. We would also not like to incorrectly classify our good messages as spam. So correctly classifying a message into spam and ham is of utmost importance. We will use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham.
  • Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set containing 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable and non-biodegradable.

Learning Objectives:

Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Cement the concepts learnt with a real-life case study on K-means Clustering

Topics Covered:

  • Clustering approaches
  • K Means clustering
  • Hierarchical clustering
  • Case Study

Hands-on:

In marketing, if you’re trying to talk to everybody, you’re not reaching anybody.This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.

Learning Objectives: 

This module will teach you about Decision Trees for regression & classification problems through a real-life case study. You will get  knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index,CHAID.The module covers basic ensemble techniques like averaging, weighted averaging & max-voting. You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting.

Going further, you will learn Random Forest with a real-life case study and learn how it helps avoid overfitting compared to decision trees.You will explore a real-life case study with heterogeneous ensemble machine learning techniques.You will gain a deep understanding of the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It covers comprehensive techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. Finally, you will examine a case study on PCA/Factor Analysis.

Topics Covered:

  • Decision Trees
  • Case Study
  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Case Study: Heterogeneous Ensemble Machine Learning
  • PCA (Principal Component Analysis) and Its Applications
  • Case Study: PCA/FA

Hands-on:

  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees).
  • In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. In this case study, use AdaBoost, GBM & Random Forest on Lending Data to predict loan status. Ensemble the output and see your resultant performance compared to a single model.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives:

This module helps you to understand hands-on implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines. The courseware covers concepts like cold-start problems.You will examine a real life case study on building a Recommendation Engine.

Topics Covered:

  • Introduction to Recommendation Systems
  • Types of Recommendation Techniques
  • Collaborative Filtering
  • Content based Filtering
  • Hybrid RS
  • Performance measurement
  • Case Study

Hands-on: 

You do not need a market research team to know what your customers are willing to buy. Netflix is an example of this, having successfully used recommender system to recommend movies to its viewers. Netflix has estimated that its recommendation engine is worth a yearly $1billion.

An increasing number of online companies are using recommendation systems to increase user interaction and benefit from the same. Build a Recommender System for a Retail Chain to recommend the right products to its users.

Projects

Predict Property Pricing using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.

Classify good and bad customers for banks to decide on granting loans.

This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans

Read More

Classify chemicals into 2 classes, biodegradable and non-biodegradable using SVM.

Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set containing 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes -

Read More

Cluster teen student into groups for targeted marketing campaigns using Kmeans Clustering.

In marketing, if you’re trying to talk to everybody, you’re not reaching anybody.. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into

Read More

Predict quality of Wine

Wine comes in various types. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees).

Note: These were the projects undertaken by students from previous batches.  

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KnowledgeHut is a great platform for beginners as well as the experienced person who wants to get into a data science job. Trainers are well experienced and we get more detailed ideas and the concepts.

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The customer support was very interactive. The trainer took a practical session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

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The trainer took a practical session which is supporting me in my daily work. I learned many things in that session with live examples.  The study materials are relevant and easy to understand and have been a really good support. I also liked the way the customer support team addressed every issue.

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Faqs

The Course

Machine learning offers computers the amazing capability of learning cumulatively from data, and making intelligent decisions based on the data, without being programmed specifically for it. This means that machines can alter their behaviour and respond intelligently by ‘thinking’ in much the same way the human brains do. R is one of the most popular programming languages for Machine learning, as its syntax is perfect for data analytics and visualization. Our Machine Learning using R workshop will help you to harness the machine learning capabilities of R and get the smart hands-on skills that employers seek.

Machine learning experts are among the most sought after professionals across the world. With Payscale putting average salaries of Deep Learning engineers at $115,034, this is definitely the space you want to be in!

Get advanced knowledge on machine learning techniques using R programmingBuild models to implement in real life business applications

By the end of this course, you would have gained knowledge on the use of machine learning techniques using R and will be able to build applications models. This will help you land lucrative jobs as a Data Scientist. 

Tools and Technologies used for this course areR programmingMS Excel

There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Machine Learning experts who have years of industry experience. 

Finance Related

Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of the written request for refund. Kindly go through our Refund Policy for more details.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

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