Location:Main Road, Bangalore


Artificial Intelligence Bootcamp In R




artificial intelligence Tech Training


Lateral Entry Professionals and Freshers


Online and Offline Classes


Week Days and Week Ends

Duration :

45 Days

artificial intelligence Objectives

•Understand the concepts in artificial intelligence
•You will learn how to install artificial intelligence.
•You will learn basics of programming in artificial intelligence
•Learn about each and every major artificial intelligence component.
•The Concepts Of artificial intelligence Language From Basic To advance
•How to build your own apps and scripts using artificial intelligence.
•Learn Basic and Advanced artificial intelligence Programming and become a artificial intelligence Developer
•An easy way to learn artificial intelligence and start coding right away!
•Learn a multitude of artificial intelligence to take your skills to the next level!

artificial intelligence bootcamp in r Training Highlights

•Advanced Topics covered with examples
•Course has been framed by Industry experts
•Fast Track course available with best Fees
•We enage Experienced trainers for Quality Training
•Highly Experienced Trainer with 10+ Years in MNC Company
•100% Guaranteed Placements Support in IT Companies with Big Salaries
•We provide one to one mentorship for the students and Working Professionals
•We do Schedule the sessions based upon your comfort by our Highly Qualified Trainers and Real time Experts

Who are eligible for artificial intelligence

•Application Server, Problem Mgmt, SAP Technical/Functional, BO Developer, Automotive Developer, Protocols, Embedded C, AutoSar, Window Applications
•Java Developer, java j2ee jsp servlets ejb, plsql, Unix Scripting, c, c++, dotnet
•Java, J2ee, Spring, Hibernate, Microservices, Node.js, Angularjs, Servlets, Sql, Cloud, Python, Ui, Ux, .Net, Asp.net, Peoplesoft, Devops, Php, Javascript
•Object Oriented Programming, Cloud Computing, Java, Testing, Web Designing, Design, Front End, Javascript, It Infrastructure, Software Development, Support
•Software Developer, IBM MDM, QA, Business Anlaysit, Business Analyst, Software Engineer, Java, Informatica, DataStage, Project Mangement


Welcome to AI in R course
•Welcome To The Course
•Install R and RStudio
•BONUS: Learning Path
•Data and Code Used in the Course
•Install MXnet in R and RStudio
•Install Mxnet in R- Written Instructions
•Install H2o
•What is Keras?
•Install Keras in R
•Working with Real Data
•Read in Data From CSV and Excel Files
•Read in Data from Online HTML Tables-Part 1
•Read in Data from Online HTML Tables-Part 2
•Working with External Data in H2o
•Remove NAs
•More Data Cleaning
•Introduction to dplyr for Data Summarizing-Part 1
•Introduction to dplyr for Data Summarizing-Part 2
•Exploratory Data Analysis(EDA): Basic Visualizations with R
•What Are the Most Common Data Types We Will Encounter?
•Some Theoretical Foundations
•Difference Between Supervised & Unsupervised Learning
•ANN Intuition
•Plan of Attack
•The Neuron
•The Activation Function
•How do Neural Networks work?
•How do Neural Networks learn?
•Gradient Descent
•Stochastic Gradient Descent
•Build Artificial Neural Networks (ANN) in R
•Neural Network for Binary Classifications
•Evaluate Accuracy
•Implement a Multi-Layer Perceptron (MLP) For Supervised Classification
•Neural Network for Multiclass Classifications
•Neural Network for Image Type Data
•Multi-class Classification Using Neural Networks with caret
•Implement an ANN with H2o For Multi-Class Supervised Classification
•Implement an ANN Based Classification Using MXNet
•Implement MLP With Keras
•Keras MLP On Real Data
•Keras MLP For Regression
•Neural Network for Regression
•More on Artificial Neural Networks(ANN) – with neuralnet
•Implement an ANN Based Regression Using MXNet
•Identify Variable Importance in Neural Networks
•Build Deep Neural Networks (DNN) in R
•Implement a Simple DNN With “neuralnet” for Binary Classifications
•Implement a Simple DNN With “deepnet” for Regression
•Implement a DNN with H2o For Multi-Class Supervised Classification
•Implement a (Less Intensive) DNN with H2o For Supervised Classification
•Implement a DNN With Keras
•Identify Variable Importance
•Implement MXNET via “caret”
•Implement a DNN with H2o For Regression
•Implement a DNN with Keras For Regression
•Implement DNN Regression With Keras (Real Data)
•Unsupervised Classification with Deep Learning
•Theory Behind Unsupervised Classification
•Autoencoders for Unsupervised Learning
•Autoencoders for Credit Card Fraud Detection
•Use the Autoencoder Model for Anomaly Detection
•Autoencoders for Unsupervised Classification
•Autoencoders With Keras
•Keras Autoencoders on Real Data
•Stacked Autoencoder With Keras
•Keras For Outlier Detection
•Find the Outlier
•Outlier Detection For Cancer (With Keras)
•CNN Intuition
•What are convolutional neural networks?
•Step 1 – Convolution Operation
•Step 1(b) – ReLU Layer
•Step 2 – Pooling
•Step 3 – Flattening
•Step 4 – Full Connection
•Softmax & Cross-Entropy
•Practical CNN Implementation in R
•Implement a CNN for Multi-Class Supervised Classification
•More About Our CNN Model Accuracy
•Set Up CNN With Keras
•More About CNN With Keras
•Implement Keras CNN On Real Images
•Some More Explanations
•Improve CNN Performance
•Working With Textual Data
•Basic Pre-Processing of Text Data
•Detect Frauds Using Keras Autoencoders on Text Data
•Word Embeddings For Classifying Fraud
•Word Embeddings For Classifying Fraud-GloVe