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!
Understand the behavior of data as you build significant models
Learn about the various libraries offered by R to manipulate, preprocess and visualize data
Supervised, Unsupervised Machine Learning and relation of statistical modelling to machine learning
Learn to use optimization techniques to find the minimum error in your machine learning model
Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail
Implement algorithms and R libraries such as CRAN-R in real world scenarios
Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction
Learn to use multiple learning algorithms to obtain better predictive performance
Learn to implement Association Rule. Use Apriori Algorithm to find associations with key metrics
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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 Grammarof 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 R 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.
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
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 -
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
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).
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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 programming
Build 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 are R programming, MS Excel.
There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers this training online.
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 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.
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