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.
Immersive Hands-on training with combination of theoretical learning, hands-on exercises, group discussions, assignments and intensive Q&A sessions.
Ask questions, get clarifications, and engage in discussions with instructors and other participants.
Get Mentored by Industry practitioners having more than 10 years of experience
Don’t gain just theoretical or practical knowledge. Understand the WHAT, WHY, and HOW of a subject. Simplify the subject matter and get in-depth comprehension.
Get reviews and timely feedback on your assignments and projects from professional developers.
We emphasize on learning the concepts through examples and help you in building a portfolio of projects through the course of training
Free lifetime enrolment into any of the upcoming batches to help you refresh the concepts
The Curriculum goes through multiple levels of design and preparation by the experts to keep the topics/modules relevant to everyday changes in technology.
Learn to use collaborative mediums to share opinions and improve your coding skills with assistance from the instructors and other participants.
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.
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.
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.
This module gives you an understanding of various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.
Learn about cost-functions using Python code.
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.
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
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.
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.
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.
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.
After completing our course, you will know:
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.
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.
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.
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 -
The training is completely hands-on and you receive the below mentioned deliverables from Zeolearn team -
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 -
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 firstname.lastname@example.org.
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.
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.
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 email@example.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 firstname.lastname@example.org. 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.
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.
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.
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.
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).