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Machine Learning with R - Certification Training
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Machine Learning with R - Certification Training

Land lucrative Machine Learning jobs by mastering R

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Modes of Delivery

Online Classroom

Collaborative, enriching virtual sessions, led by world class instructors at time slots to suit your convenience.

Classroom

Our classroom training provides you the opportunity to interact with instructors and benefit from face-to-face instruction.

Team/Corporate Training

Our Corporate training is carefully structured to help executives keep ahead of rapidly evolving business environments.
Group Discount: 10.00% for 2 people 15.00% for 3 to 4 people 20.00% for 5 and above people

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

Curriculum

Module 1- Statistical Learning

Learning Objectives: Visit basics like mean (expected value), median and mode
Distribution of data in terms of variance, standard deviation and interquartile range
Basic summaries about the data and the measures. Together with simple graphics analysis
Basics of probability with daily life examples
Marginal probability and its importance with respective to datascience
Learn baye's theorem and conditional probability
Learn alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value

Topics

  • 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
Module 2- R for Machine Learning

Learning Objectives:

  • Get an idea of what is R. Why R is so popular tool among Data Scientists.
  • Learn how to install R and its components.
  • Learn how to install and load libraries
  • Learn frequently used libraries
  • Learn about data structures in R
  • Learn all about loops and control statements in R
  • Learn how to write custom functions, nested functions and functions with arguments
  • Lean all about loop functions available in R which are efficient and can be written with single command
  • Learn all about string manipulations and regular expressions. The functions can be extremely useful for text or unstructured data manipulations
  • Learn how to import data from various sources in R. How to write files from R. How to connect to various databases from R
  • Learn visualization in R with base and ggplot libraries. Learn Grammar of Graphics in a very structured easy-to-understand manner
  • Hands-on session on a real-life case study
Topics

  • 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
Module 3- Introduction to Machine Learning

Learning Objectives: Look at real-life examples of Machine Learning and how it affects society in ways you may not have guessed! Explore many algorithms and models like Classification, Regression, Clustering. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning

Topics

  • 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 model
Module 4- Optimization

Learning Objectives:  Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp

Topics

  • Maxima and Minima
  • Cost Function
  • Learning Rate
  • Optimization Techniques
Hands-on: Learn about cost-functions using python code

Module 5- Supervised Learning

Learning Objectives:  Learn Linear Regression with Stochastic Gradient Descent with real-life case study. Covers hyper-parameters tuning like learning rate, epochs, momentum
Real Life Case Study on Linear Regression
Learn Logistic Regression with Stochastic Gradient Descent with real-life case study. Covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance
Real Life Case Study on Logistic Regression
Learn how KNN can be used for a classification problem
Real Life Case Study on KNN Classification
Real Life Case Study on Naive Bayesian Classifiers
Learn how Support Vector Machines can be used for a classification problem with real-life case study. Covers hyper-parameter tuning like regularization
Real Life Case Study on SVM

Topics

  • Linear Regression
  • Case Study
  • Logistic Regression
  • Case Study
  • K-NN Classification
  • 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.

Module 6- Unsupervised Learning

Learning Objectives: Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering
Real Life Case Study on K-means Clustering

Topics

  • 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.


Module 7- Ensemble Techniques

Learning Objectives:  Decision Trees - for regression & classification problem. Covers both Classification & regression problem. Candidates get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID
Real Life Case Study on Decision Tree
Cover basic ensemble techniques like averaging, weighted averaging & max-voting
Learn about bootstrap sampling and its advantages followed by bagging.
Boost model performance with Boosting
Learn Random Forest with real-life case study and how it helps avoid overfitting compared to decision trees
Real life case study with heterogeneous ensemble machine learning techniques
Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. Covers techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion
Real-Life case study with PCA & FA

Topics

  • 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 style. 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 result perform than a single model
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling
Module 8- Recommendation System

Learning Objectives: Hands-on implementation of Association Rules. Use Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Learn what is UBCF and how is it used in Recommender Engines. Covers concepts like cold-start problems. Learn what is IBCF and how is it used in Recommender Engines
Real Life Case Study with Recommender Systems

Topics

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

  • Hands-on implementation of Association Rules. Use Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Learn what is UBCF and how is it used in Recommender Engines. Covers concepts like cold-start problems. Learn what is IBCF and how is it used in Recommender Engines
  • Real Life Case Study with Recommender Systems
Projects
Covers projects using Linear Regression, Logistic Regression, Decision Tree, Time Series Forecasting, K-Nearest Neighbor, Support Vector Machine, Adaboost, GBM, Random Forest etc.

Key Features

50 hours of Instructor led Training
Comprehensive Hands-on with R programming
Covers Supervised learning algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Decision Trees
Covers Unsupervised learning algorithms such as K-means clustering techniques
Covers implementation of Association Rules

Our Students See All

Attended a 2 day weekend course by Knowledgehut for the CSM certification. The instructor was very knowledgeable and engaging. Excellent experience.

Attended workshop in April 2018

The CSPO Training was awesome and great. The trainer Anderson made all the concepts look so easy and simple. Using his past experience as examples to explain various scenarios was a plus. Moreover, it was an active session with a lot of participant involvement which not only made it interactive but interesting as well. Would definitely recommend this Training.

Attended workshop in July 2018

Great course. An interesting and interactive session to better understand how to succeed in formulating a business case and how to present it effectively.

Attended workshop in May 2018

The training was very interactive and engaging with the attendees.

Attended workshop in June 2018
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Jin Shi

Director at Timber creek Asset Management from Toronto, Canada
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Richard Dsouza

Business Analyst at Valtech from Bangalore, India
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Wily Salim

Services Project Engineer at Lendlease from Sydney, Australia
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Anish Maidh

Senior Project Manager at Telstra from Melbourne, Australia

Frequently Asked Questions

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!

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.

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.

Any registration cancelled 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 written request for refund. Kindly go through our Refund Policy for more details: http://www.knowledgehut.com/refund

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.

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

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

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