Machine Learning with R Rated 4.0/5 based on 469 customer reviews

Machine Learning with R Training in Washington-DC, United States

A comprehensive Machine learning with R training course from Zeolearn.

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

Why should you learn Machine Learning with R?

Machine learning has forever changed the technology landscape. ML helps to build smart and intelligent machines that work without human intervention and continuously learn, evolve and improve by taking in new data.  Whether it’s in education, healthcare, transport or government sectors, ML will disrupt industries and ensure more efficient outputs. As the global market for AI and ML expands, so does the need for professionals with expertise in ML programming. 

R is a language that is most suited for ML programming and achieving mastery in it is paramount for a career in ML. This is an extremely exciting field, not to mention that with such a huge market share most jobs would be in the field of AI and ML. Master ML and become part of the technology revolution that will shape the future world. 

How do you get started with ML with R?

If you are new to ML and R and wish to go from basic to advanced in order to start your career in this field, then this comprehensive course from Zeolearn is just right for you. You will learn all the concepts of R and ML along with Supervised vs Unsupervised Learning, the ways in which Statistical Modeling relates to Machine Learning, and a comparison of each using R libraries. With extensive hands-on practical work and assignments you will learn to build systems that learn from experience, and exploit data to create simple predictive models with real world relevance. 

Learn from mentors who have extensive knowledge of AI, machine learning and the R programming language. There is no time like now to get started on this learning path.

What you will learn:

  • Statistical Learning: Understand the behavior of data as you build significant models
  • R for Machine Learning: Learn about the various libraries offered by R to manipulate, preprocess and visualize data
  • Fundamentals of Machine Learning: Supervised, Unsupervised Machine Learning and relation of statistical modelling to machine learning
  • Optimization Techniques: Learn to use optimization techniques to find the minimum error in your machine learning model
  • Machine Learning Algorithms: Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail
  • Build Models: Implement algorithms and R libraries such as CRAN-R in real world scenarios
  • Dimensionality Reduction: Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction
  • Ensemble Learning: Learn to use multiple learning algorithms to obtain better predictive performance
  • Recommendation systems: Learn to implement Association Rule. Use Apriori Algorithm to find associations with key metrics
Prerequisites
  • Elementary programming knowledge
  • Familiarity with statistics

Who should Attend?

  • Those interested in ML and R programming
  • Software or Data Engineers interested in learning quantitative analysis and ML

Zeolearn Experience

Learn By Doing

Immersive Hands-on training with combination of theoretical learning, hands-on exercises, group discussions, assignments and intensive Q&A sessions.

Live & Interactive

 Ask questions, get clarifications, and engage in discussions with instructors and other participants.

Mentored by Industry Experts

Get Mentored by Industry practitioners having more than 10 years of experience

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

  • 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 Grammar of Graphics in a very structured easy-to-understand manner. The module ends with a 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

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

  • 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

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

Hands-on :

Learn about cost-functions using Python 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

  • 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

  • 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

  • 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 result perform better than 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

  • 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.

FAQs

The Course

After completing our course, you will know:

  • About R, its basic data structures, objects and operations
  • Basic plots and lattice plots
  • Basic statistics and machine learning fundamentals
  • Recommendation systems
  • Support Vector Machine, Adaboost, GBM, Random Forest etc.
  • Common classifier algorithms such as Decision Tree

Machine learning is often used to make informed preventive or proactive decisions with its powerful predictive capabilities. With the help of algorithms, machine learning helps find insights and patterns in data. This automatic application of complex mathematical calculations to big data is revolutionising the world. R is a programming language that is the most popular platform for applied machine learning and is widely used to statistically explore data sets. Studying R in machine learning will help you become a proficient data analyst. 

  • Elementary programming knowledge
  • Familiarity with statistics

This course is for anyone wanting to apply ML algorithms to real life business problems. Also, Software or Data Engineers interested in learning quantitative analysis and ML.

Machine Learning with R workshop experience

The workshops at Zeolearn are always interactive, immersive and intensive hands-on programs. There are 3 modes of Delivery and you can select based on the requirements - 

Online Classroom training: Learn from anywhere through the most preferred virtual live instructor led training with the help of hands-on training and interactive sessions   

One-to-One Training: You can enrol for one-to-one Machine Learning with R classroom training session with our expert trainer at a preferred time. With this mode, you can customize your curriculum to suit your learning needs. 

Team/Corporate Training: In this type of training, an Organization can nominate their entire team for online or classroom training. You can customize your curriculum to suit your learning needs and also get post-training expert’s support to implement Machine Learning with R concepts in the project.  

We follow the below mentioned procedure for all the training programs by dividing the complete workshop experience into 3 stages i.e Pre, Workshop and Post. This is a tried and tested approach using which we have been able to upskill thousands of engineers.

Pre-training 

Before the start of training program, we make sure that you are ready to understand the concepts from Day 1. Hence, as a process of preparation for the intensive workshop, we provide the following -

  • Reference articles/ Videos and e-books
  • 2-4 hrs of training on pre-requisites - to make you workshop ready
  • Pre-Workshop Assessments - to assess and benchmark 
  • Environment set-up docs 

During Training

The training is completely hands-on and you receive the below mentioned deliverables from Zeolearn team - 

  • PPT and Code Snippets used in the class
  • Learners Guide or E-book
  • Projects / Case Studies
  • Assessments / Lab exercises
  • Quizzes and Polls
  • Study Plans - To structure your learning.

Post Training

We don’t just impart skills but also make sure that you implement them in the project. And for that to happen, we are always in touch with you either through newsletters or webinars or next version trainings. Some of the post-training deliverables lined-up for you are - 

  • Project assistance with mentor
  • Course Recordings 
  • Access to Alumni Network
  • Additional workshops on advanced level concepts 
  • Regular emails/newsletters on Blogs/Tutorials and other informational content

This course is delivered by industry-recognized experts who would be having more than 10 years of real-time experience in Machine Learning with R.

Not only will they impart knowledge of the fundamentals and advanced concepts, they will provide end-to-end mentorship and hands-on training to help you work on real-world projects with regards to Machine Learning with R.

Once you register for the course you will be provided with system requirements and lab setup document which contains detailed information to prepare the environment for the course.

To attend Machine Learning with R training program, the basic hardware and software requirements are as mentioned below - 

Hardware requirements - 8 GB RAM, i3 Processor or equivalent

Software Requirements – MAC OS or Windows

Machine Learning with R Online Training Experience

All our training programs are quite interactive and fun to learn with plenty of time spent on lot of hands-on practical training, use case discussions and quizzes. Our instructors also use an extensive set of collaboration tools and techniques which improves your online training experience.

This will be live interactive training led by an instructor in a virtual classroom.

You will receive a registration link to your e-mail id from our training delivery team. You will have to log in from your PC or other devices.

Yes, for all the online public workshops there would be participants logging in from different locations.

In case of any queries, you can reach out to our 24/7 dedicated support at any of the numbers provided in the link below: http://www.zeolearn.com/contact-us, or send an email to hello@zeolearn.com.

We also have Slack workspace for the corporates to discuss the issues. If the query is not resolved by email, we will facilitate a one-on-one discussion session with our trainers.

If you miss a class, you can access the class recordings anytime from our LMS. At the beginning of every session, there will be a 10-12 minute recapitulation of the previous class. You can watch the online recording and clarify your doubts at that time. You may need to login 15 minutes before the main lecture begins to avail this facility.

We also have a Free Lifetime enrollment for most of our courses. In case you miss out a class, you can also enroll for another complete workshop or only for a particular session.

Finance related

Typically, Zeolearn’s training are exhaustive and the mentors help you out in understanding the in-depth concepts. 

However, if you find it difficult to cope, you may discontinue within the first 4 hours of training and avail a 100% refund. Learn more about our refund policy here.

Zeolearn offers a 100% money back guarantee if the candidates withdraw from the course right after the first session. To learn more about the 100% refund policy, visit our refund page.

Yes, we have scholarships available for Students and Veterans. We do provide grants that can vary upto 50% of the course fees. 

To avail scholarships, please get in touch with us at hello@zeolearn.com. The team shall send across the forms and instructions to you. Based upon the responses and answers that we receive, the panel of experts take a decision on the Grant. The entire process could take around 7 to 15 days.

Yes, we do have instalment options available for the course fees. To avail instalments, please get in touch with us at hello@zeolearn.com. The team shall explain how the instalments work and would provide the timelines for your case.

Usually we allow payment in 2 to 3 instalments but have all to be paid before you complete the course.

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 to its customers.

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 contains 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.

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 segments for targeted marketing campaigns.

Predict quality of Wine

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

Have More Questions?

Machine Learning with R Course in Washington-DC

Machine Learning using R

 

The most frequently used platform to settle on informed preventive or proactive choices with its full prescient capacities is known as Machine Learning. This open-source programming dialect is utilised for measurements, information mining, give speedy outcomes and is bolstered by an overall network of clients and designers. If you want to turn into an analyst, then register in machine learning using R training in Washington DC offered by Zeolearn institute. An expert tutor administered in machine learning using R training in Washington DC leads and gives lectures on the fundamentals of R dialect and machine learning prologue, its tools, framework, and testing ideas etc.

 

How this course benefits you?

 

With the assistance of calculations shown in machine learning using the R course in Washington DC, you will learn how machine learning benefits in discovering intuitions and designs in information. Learning how to contemplate R in machine learning will enable you to end up as a capable information analyst and have a great career in this profession. An intensive workshop is equipped in machine learning using R course in Washington DC to enhance your aptitudes on machine learning concepts and its essential measurements using R. You will understand about R, its vital information structures, articles, and tasks. You will study about the grouping of information utilising Naïve-Bayes classifier.

 

Key features of machine learning course

 

The instructor driven machine learning classes using R in Washington DC is for 40 hours with serious preparation. You will envision the basics and propelled ideas of R condition. Make sense of how extensive the anticipating shapes and independent learning are executed in R. You will handle how the R dialect condition underpins by the prescient displaying with machine learning. Attend the classes of machine learning in Washington DC based on your convenient schedule. The machine learning specialists will direct understudies enrolled in this course for executing the innovation for future tasks to become an investigator. Zeolearn offers you machine learning certification using R in Washington DC after the successful completion of this course.

 

 

 

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