Contact Us

Hide

Online course detail

Machine Learning with R Programming

R is the #1 Google Search for Advanced Analytics software. It is growing faster than any other data science language and so are its users (expected to reach 10 million by 2020). Hence, R is a fascinating programming language that has become an appealing skill to add to your resume. Training will make you an expert in R Programming language, Data Manipulation, Data Visualization, Exploratory Data Analysis, Data Mining, Correlation & Regression, Sentimental Analysis.

Instructor Led Training  |  Free Course Repeat  |  Placement Assistance  |  Job Focused Projects  |  Interview Preparation Sessions

Read Reviews

Get Free Consultation

Curriculum

Topics to be covered

    • Statistical learning vs. Machine learning
    • Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
    • Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)
    • Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
    • Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)
    • Iteration and Model Evaluation
    • rpart
    • randomForest
    • mlr3
    • MICE
    • Dplyr
    • PARTY
    • ctree
    • CARET
    • nnet
    • kernLab
    • Data Exploration Techniques
    • Sea-born | Matplot
    • Correlation Analysis
    • Data Wrangling
    • Outliers Values in a DataSet
    • Data Manipulation
    • Missing & Categorical Data
    • Splitting the Data into Training Set & Test Set
    • Feature Scaling
    • Concept of Over fitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
    • Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)
    • Basic Data Structure & Data Types in Python language.
    • Working with data frames and Data handling packages.
    • Importing Data from various file sources like csv, txt, Excel, HDFS and other files types.
    • Reading and Analysis of data and various operations for data analysis.
    • Connecting with database (MySql, Oracle).
    • Exporting files into different formats.
    • Data Visualization and concept of tidy data.
    • Handling Missing Information.
    • Calls Data Capstone Project
    • Finance Project : Perform EDA of stock prices. We will focus on BANK Stocks(JPMorgan, Bank Of America, Goldman Sachs, Morgan Stanley, Wells Fargo) & see how they progressed throughout the financial crisis all the way to early 2016.
    • Fundamental of descriptive Statistics and Hypothesis testing (t-test, z-test).
    • Probability Distribution and analysis of Variance.
    • Correlation and Regression.
    • Linear Modeling.
    • Advance Analytics.
    • Poisson and logistic Regression
    • Feature Selection
    • Principal Component Analysis(PCA)
    • Linear Discriminant Analysis (LDA)
    • Kernel PCA
    • Feature Reduction
    • Simple Linear Regression
    • Multiple Linear Regression
    • Regularization
    • Generalization & Non Linearity
    • Recursive Partitioning (Decision Trees)
    • Ensemble Models (Random Forest , Bagging & Boosting (ADA, GBM)
    • Ensemble Learning Methods
    • Working of Ada Boost
    • AdaBoost Algorithm & Flowchart
    • Gradient Boosting
    • XGBoost
    • Polynomial Regression
    • Support Vector Regression
    • Decision Tree Regression
    • Evaluating Regression Models Performance


    • Logistic Regression
    • K-Nearest Neighbours(K-NN)
    • Support Vector Machine(SVM)
    • Kernel SVM
    • Naive Bayes
    • Decision Tree Classification
    • Random Forest Classification
    • Evaluating Classification Models Performance


    Clustering

    • K-Means Clustering
    • Challenges of Unsupervised Learning and beyond K-Means
    • Hierarchical Clustering
    • Purpose of Recommender Systems
    • Collaborative Filtering
    • Association Rule Mining : Market Basket Analysis
    • Association Rule Generation : Apriori Algorithm
    • Apriori Algorithm : Rule Selection
    • Movie Recommendation
    • Book Rental Recommendation
    • Apriori
    • Eclat
    • HMM Introduction : Why do we use HMM?
    • The Markov Property
    • The Math Of Markov Chains
    • From Markov Models to Hidden Markov Models (HMM)
    • HMM Basic Examples
    • Parameters of an HMM
    • Forward-Backward Algorithm
    • The Viterbi Algorithm
    • HMM Training
    • How to choose number of Hidden States
    • Baum-Welch Updates for Multiple Observations
    • Discrete HMM in Code
    • Discrete HMM Updates with Scaling
    • Scaled Viterbi Algorithm in Log Space
    • Gradient Descent
    • Theano Scan
    • Discrete HMM in Theano
    • Improving our Gradient Descent-Based HMM
    • TensorFlow Scan
    • Discrete HMM IN TensorFlow
    • Gaussian Mixture Models with Hidden Markov Models
    • Generating Data from a Real-Valued HMM
    • Continuous-Observation HMM
    • Continuous HMM in Theano
    • Continuous HMM in TensorFlow
    • Generative Vs. Discriminative Classifiers
    • HMM Classification on Poetry Data(Robert Frost vs Edgar Allan Poe)
    • Upper Confidence Bound (UCB)
    • Thompson Sampling
    • Spacy Basics
    • Tokenization
    • Stemming
    • Lemmatization
    • Stop-Words
    • Vocabulary-and-Matching
    • NLP-Basics Assessment
    • NLP Implementations
    • NLP Libraries
    • Tokenize & Remove words using NLTK
    • Get Synonyms & Antonyms from WordNet
    • Installing NLTK in Python
    • Tokenizing Words and Sentences
    • How tokenization works? - Text
    • Introduction to Stemming & Lemmatization
    • Stemming using NLTK
    • Lemmatization using NLTK




    • Stop word removal using Latent NLTK
    • Parts of Speech Tagging
    • POS Tag Meanings
    • Named Entity Recognition
    • Text Modelling using Bag of Words Model
    • Building a BOW Model
    • Text Modelling using TF-IDF Model
    • Building the TF-IDF Model
    • Understanding the N-Gram Model
    • Building Character N-Gram Model
    • Building Word N-Gram Model
    • Understanding Latent Semantic Analysis
    • LSA in Python
    • Word Synonyms and Antonyms using NLTK
    • Word Negation Tracking in Python
    • Understanding Word Vectors
    • Training the Word2Vec Model
    • Exploring Pre-trained Models
    • POS-Basics
    • Visualizing POS
    • NER-Named-Entity-Recognition
    • Visualizing NER
    • Sentence Segmentation
    • Text Classification
      • Feature Extraction from Text
      • Text Classification Project
    • Semantics-and-Sentimental-Analysis
      • Semantics and Word Vectors
      • Sentiment Analysis
      • Sentimental Analysis Project
    • Artificial Neural Networks
    • Convolutional Neural Networks
    • Random Forest, Bagging & Boosting (ada, gbm etc)
    • Ensemble Learning Methods
    • Working of AdaBoost
    • AdaBoost Algorithm & Flowchart
    • Gradient Boosting
    • XGBoost
    • Model Selection Section
    • XGBoost


    • Business Decisions and Analytics
    • Types of Business Analytics
    • Descriptive (Explains what happened)
    • Diagnostic (Explains why it happened)
    • Predictive (Forecasts what might happen)
    • Prescriptive (Recommends an action based on forecast)
    • Artificial Intelligence (How to enhance or replace human reasoning?)
    • Applications of Business Analytics
    1. Text Classification
    2. Twitter Sentimental Analysis
    3. Text Summarization

Course Description

    In order to perform most complicated and intricate analysis, R is one of the easiest language for statisticians without getting into too much of details. With so many benefits for data science, R has gradually mounted heights among professionals of big data. WE understand that it can be an intimidating course and hence we have devised a systematic course to make sure you understand it properly.

    Gyansetu’s Data Analytics with R training course is designed to build an expertise in Business Intelligence, Data Analytics, Text Analytics & Machine Learning. R Certification provides high level graphical capabilities and packages with statistical models. It also include concepts of Statistical Learning, Hypothesis Testing, Predictive Analysis, Topic Modelling.

    After the completion of the Gyansetu Data Analytics with R course, you should be able to:

    1. Understand the concepts of data Analytics and Text Analytics.
    2. Descriptive Statistical Learning and Multivariate Data Analysis.
    3. Basic difference between the R and other Analytical Languages (SAS/SPSS)
    4. Project Building and Working in R environment (GUI Support)
    5. Data Analysis and Data Handling (Using different datasets, packages and functions)
    6. Importing and Exporting various data structures (data frames, csv , excel sheets , xml)
    7. Graph generation and Visualization (2-dimension and 3- dimension)
    8. Predictive Modeling (Using Machine Learning classification/ Clustering Algorithm)
    9. Text Analytics and Word Graph Generation (tm, NLP packages)
    10. Mapping with Hadoop ( R Hadoop package)
    11. Interactive Visualization with Tableau

    Gyansetu Data Science program is delivered by faculty having a strong educational M.Tech (CS) from IIT-Hyderabad, B.Tech (CS) from NIT-Surat (Gold Medalist) & currently working with world's top IT company Microsoft.

    We at Gyansetu understand that teaching any course is not difficult but to make someone job ready is the essential task. That's why we have prepared capstone projects which will drive your learning through real time industry scenarios and help you clearing interviews.

    Knowledge of basic statistics & any programming language is beneficial. However, Gyansetu offers a complementary instructor led course on statistics & R programming before you start Data Science course.

    Gyansetu is providing complimentary placement service to all students. Gyansetu Placement Team consistently work on industry collaboration and associations which help our students to find their dream job right after the completion of training.

    • Our placement team will add Big Data skills & projects in your CV and update your profile on Job search engines like Naukri, Indeed, Monster, etc. This will increase your profile visibility in top recruiter search and ultimately increase interview calls by 5x.
    • Our faculty offers extended support to students by clearing doubts faced during the interview and preparing them for the upcoming interviews.
    • Gyansetu’s Students are currently working in Companies like Sapient, Capgemini, TCS, Sopra, HCL, Birlasoft, Wipro, Accenture, Zomato, Ola Cabs, Oyo Rooms, etc.


    ???????

    • Gyansetu trainer’s are well known in Industry; who are highly qualified and currently working in top MNCs.
    • We provide interaction with faculty before the course starts.
    • Our experts help students in learning Technology from basics, even if you are not good in basic programming skills, don’t worry! We will help you.
    • Faculties will help you in preparing project reports & presentations.
    • Students will be provided Mentoring sessions by Experts.

Certification

Machine Learning Certification

APPLY NOW

Reviews

Placement

Enroll Now

Structure your learning and get a certificate to prove it.

Machine Learning with R Programming Features

FAQs

    We have seen getting a relevant interview call is not a big challenge in your case. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. We help you prepare your CV by adding relevant projects and skills once 80% of the course is completed. Our placement team will update your profile on Job Portals, this increases relevant interview calls by 5x.

    Interview selection depends on your knowledge and learning. As per the past trend, initial 5 interviews is a learning experience of

    • What type of technical questions are asked in interviews?
    • What are their expectations?
    • How should you prepare?


    Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

    We have seen getting a technical interview call is a challenge at times. Most of the time you receive sales job calls/ backend job calls/ BPO job calls. No Worries!! Our Placement team will prepare your CV in such a way that you will have a good number of technical interview calls. We will provide you interview preparation sessions and make you job ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. Our placement team will update your profile on Job Portals, this increases relevant interview call by 3x

    Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews is a learning experience of

    • What type of technical questions are asked in interviews?
    • What are their expectations?
    • How should you prepare?


    Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.


    We have seen getting a technical interview call is hardly possible. Gyansetu provides internship opportunities to the non-working students so they have some industry exposure before they appear in interviews. Internship experience adds a lot of value to your CV and our placement team will prepare your CV in such a way that you will have a good number of interview calls. We will provide you interview preparation sessions and make you job ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training and we will update your profile on Job Portals, this increases relevant interview call by 3x

    Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews is a learning experience of

    • What type of technical questions are asked in interviews?
    • What are their expectations?
    • How should you prepare?


    Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

    Yes, a one-to-one faculty discussion and demo session will be provided before admission. We understand the importance of trust between you and the trainer. We will be happy if you clear all your queries before you start classes with us.

    We understand the importance of every session. Sessions recording will be shared with you and in case of any query, faculty will give you extra time to answer your queries.


    Yes, we understand that self-learning is most crucial and for the same we provide students with PPTs, PDFs, class recordings, lab sessions, etc, so that a student can get a good handle of these topics.

    We provide an option to retake the course within 3 months from the completion of your course, so that you get more time to learn the concepts and do the best in your interviews.

    We believe in the concept that having less students is the best way to pay attention to each student individually and for the same our batch size varies between 5-10 people.

    Yes, we have batches available on weekends. We understand many students are in jobs and it's difficult to take time for training on weekdays. Batch timings need to be checked with our counsellors.

    Yes, we have batches available on weekdays but in limited time slots. Since most of our trainers are working, so either the batches are available in morning hours or in the evening hours. You need to contact our counsellors to know more on this.

    Total duration of the course is 160 hours (80 hours of live instructor-led-training and 80 hours of self-paced learning).

    You don’t need to pay anyone for software installation, our faculties will provide you all the required softwares and will assist you in the complete installation process.

    Our faculties will help you in resolving your queries during and after the course.

Relevant interested Courses