Machine Learning with R Programming

• 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

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 Machine Learning 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.