Prerequisites:  Any graduating candidate can join

Certificate: Certificate of Internship

ToC for Internship & Training:

Module -1 – Introduction to Course

• Introduction to Data Analytics
• Real time use and application 
• Career opportunities 

Module 2 – Python Programming Basics 

• Getting started with Python
• What is Python?
• Installing Anaconda
• Variables, and Data Structure
• List, tuples and dictionary
• Control Structure
• Functions in python
• Lambda functions
• Object Oriented Programming Modules
• Using Packages Os package, time and datetime
• File Handling in Python
• Miscellaneous Functions in python 

Module 3 – Statistics for Data Science 

• Introduction to Statistics
• Population and Sample
• Descriptive Statistics v/s Inferential Statistics
• Types of variable
• Categorical and Continuous Data
• Ratio and Interval
• Nominal and Ordinal Data
• Descriptive Statistics
• Measure of Central Tendency – Mean, Mode and Median
• Percentile and Quartile
• Measure of Spread – IQR, Variance and Standard Deviation
• Coefficient of Variation
• Measure of Shape – Kurtosis and Skewness
• Correlation Analysis
• Inferential Statistics
• Empirical Rule & Chebyshev’s Theorem
• Z Test
• One Sample T test, independent t test
• ANOVA – f test
• Chi Square test

Module 4 – Working with numpy & Pandas 

• Working with Numpy
• NumPy Overview
• Properties, Purpose, and Types of ndarray
• Class and Attributes of ndarray Object
• Basic Operations: Concept and Examples
• Accessing Array
• Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
• Shape Manipulation & Broadcasting
• Linear Algebra using numpy
• Stacking and resizing the array
• random numbers using numpy
• Working with Pandas
• Data Structures
• Series, DataFrame & Panel
• DataFrame basic properties
• Importing excel sheets, csv files, executing sql queries
• Importing and exporting json files
• Data Selection and Filtering
• Selection of columns and rows
• Filtering Dataframes
• Filtering – AND operaton and OR operation

Module 5 – Working with numpy and Pandas

• Working with Pandas
• Data Cleaning
• Handling Duplicates
• Handling unusual values
• handling missing values
• Finding unique values
• Descriptive Analysis with pandas
• Creating new features
• Creating new categorical features from continuous variable
• combining multiple dataframes
• groupby operations
• groupby statistical Analysis
• Apply method
• String Manipulation 

Module 6 – Data Visualization

• Basic Visualization with matplotlib
• Matplotlib Features
• Line Properties
• Plot with (x, y)
• Controlling Line Patterns and Colors
• Set Axis, Labels, and Legend Properties
• Alpha and Annotation
• Multiple PlotsSubplots
• Advance visualization using seaborn
• Types of Plots and Seaborn
• Boxplots
• Distribution Plots
• Countplots
• Heatmaps
• Voilin plots
• Swarmplots and pointplots

Module 7 – Capstone Project

• Data Science Standard Project
• Data Science Project Life cycle
• Project Topic
• Data Capturing
• Data Cleaning 
• Data Analytics
• Working on tools
• Data Visualization tools
• Project Report Completion

Let’s talk to us if you have any query

Pre-requisites: Basic Understanding of Python

Certificate: Certificate of Internship

ToC for Internship & Training:

Module 1 – Linear Regression

• The conceptual idea of linear regression
• Predictive Equation
• Cost function formation
• Gradient Descent Algorithm
• OLS approach for Linear Regression
• Multivariate Regression Model
• Correlation Analysis – Analyzing the dependence of variables
• Apply Data Transformations
• Overfitting 
• L1 & L2 Regularization
• Identify Multicollinearity in Data Treatment on Data
• Identify Heteroscedasticity Modelling of Data
• Variable Significance Identification
• Model Significance Test
• R2, MAPE, RMSE
• Project: Predictive Analysis using Linear Regression 

Module 2 – Logistic Regression

• Classification Problem Analysis
• Variable and Model Significance
• Sigmoid Function
• Cost Function Formation
• Mathematical Modelling 
• Model Parameter Significance Evaluation
• Implementing logistic regression using sklearn
• Performance analysis for classification problem
• Confusion Matrix Analysis
• Accuracy, recall, precision and F1 Score
• Specificity and Sensitivity
• Drawing the ROC Curve
• AUC for ROC 
• Classification Report Analysis
• Estimating the Classification Model
• Project: Predictive Analysis using Logistic Regression

Module 3 – KNN & Decision Tree K Nearest Neighbour

• Understanding the KNN
• Distance metrics
• KNN for Regression & classification
• Implementing KNN using Python
• Case Study on KNN
• Handling overfitting and underfitting with KNN
• Decision Tree
• Forming Decision Tree
• Components of Decision Tree
• Mathematics of Decision Tree
• Entropy Approach
• Gini Entropy Approach
• Variance – Decision Tree for Regression
• Decision Tree Evaluation
• Overfitting of Decision Tree
• Handling overfitting using hyperparameters
• Hyperparameters tuning using gridsearch
• VIsualizing Decision Tree using graphviz

Module 4 – SVM & Ensemble Learning

• Support Vector Machines
• Concept and Working Principle
• Mathematical Modelling
• Optimization Function Formation
• Slack Variable
• The Kernel Method and Nonlinear Hyperplanes
• Use Cases
• Programming SVM using Python
• Project – Character recognition using SVM

Module 5 – Ensemble Learning

• Concept of Ensemble Learning 
• Bagging and Boosting
• Bagging – Random Forest
• Random Forest for Classification
• Random Forest for Regression
• Boosting – Gradient Boosting Trees
• Boosting – Adaboost
• Boosting – XGBoost
• Stacking

Module 6 – Unsupervised Learning Clustering 

• Application of clustering
• Hierarchical Clustering
• K Means Clustering
• Use Cases for K Means Clustering
• Programming for K Means using Python
• Customer segmentation using KMeans
• Cluster Size Optimization vs Definition Optimization
• Projects & Case Studies
• Dimensionality Reduction – PCA
• Dimensionality Reduction, Data Compression
• Curse of dimensionality
• Multicollinearity
• Factor Analysis
• Concept and Mathematical modelling
• Use Cases

Module 7 –Capstone Project

• Working Final Project
• Splitting final Project into phases
• Working on structuring project
• Do’s and Don’ts with Machine Learning

Let’s talk to us if you have any query

Prerequisites: 

• Working knowledge of Python programming
• Basic understanding of Machine Learning algorithms
• Understanding of working with Data
• Linear Algebra and Matrix calculations

Certificate: Certificate of Internship

ToC for Internship & Training:

Module – 1 – Overview of AI, ML & Deep Learning

• Artificial Intelligence & Machine Learning Introduction
• Computer Vision Overview
• Applications of Computer Vision
• Deep Learning Introduction
• Deep learning v/s Machine Learning
• GPU for Deep Learning
• Applications of Deep Learning
• Algorithms and use cases in Deep Learning

Module 2 – Artificial Neural Networks

• Introduction to Neural Networks
• Working of Neural Networks
• Mathematical modelling of Neural Networks
• Architectures of ANN
• ANN learning process
• Gradient Descent Algorithm
• Cost Function formation
• Python programming example
• Softmax Classifier & ReLU Classifier
• Dropout Optimization
• Mathematics behind backpropagation
• Deep Neural Networks using Tensorflow
• Activation Functions for Neural Networks
• Optimization Techniques – SGD, ADAM, LBFGS
• Controlling Overfitting
• Regularization
• Momentum in Neural Networks
• Neural Network Tuning and Performance Optimization

Module 3 – Working with Tensorflow & Pytorch

• Introduction to TensorFlow & keras
• The Programming Model
• Data Model, Tensor Board
• Programming Neural Networks with Tensorflow
• Working with Pytorch
• Basics of Pytorch
• Programming model of Pytorch
• Building Linear Regression and Logistic Regression with Pytorch
• Examples and use cases

Module 4 – Text and Speech pre-processing

• Working with speech to text and text to speech conversions
• Text pre-processing with Regular Expressions
• Working with NLTK
• Tokenization, stemming, lemmatization
• PoS Tagging
• Spelling correction
• Working with spaCy
• NER
• Dependency Parsing
• Document Similarity Matching

Module 5 – Text Classification

• Text Classification – Vectorization Techniques
• Tf-IDF, count vector co-occurence Vectorization
• N-gram feature modelling
• Feature Pruning methods
• Project: Sentiment Analysis
• Project: Building an Email Classification Model

Module 6 – Social Media Analytics

• Collecting live tweets from twitter
• Cleaning and processing tweets
• Information extraction from tweets
• Converting unstructured data to semi structured
• Analysing tweet data
• Project: Streaming live tweets and Sentiment Analysis
• Wordcloud

Module 7 – RNN and LSTM

• Recurrent Neural Networks
• Basic concepts of RNN
• Unfolding Recurrent Neural Networks
• The Vanishing Gradient Problem
• The Exploding Gradient Problem
• LSTM Networks
• Understanding of LSTM
• Erase Gate, Input Gate and Read Gate
• Overview of GRU
• Case study
• Implementing LSTM using Tensorflow-keras
• Implementing LSTM using Pytorch
• Text Classification using NLP
• Developing a chatbot application using NLP

Module 8 – Transfer Learning for NLP

• Understanding the concept of Transfer Learning for NLP
• General issues with NLP problems
• Ideas for transfer learning approach for different situations in case of NLP 
• Using Universal Sentence Encoders
• Deep Averaging Networks
• Sequence to Sequence Models
• Encoder and Decoder Understanding
• Attention-based models
• Transformer Models – Self Attention models
• Using ELMo for word vectorization
• Working of BERT
• Working of GPT-2
• Working of XLNet
• Using BERT, GPT-2, XLNet for text classification

Module 9 – Image Processing

• Getting started with Images
• Image Processing with OpenCV
• Image Acquisition and manipulation using OpenCV
• Video Processing
• Edge Detection
• Corner Detection
• Color Detection
• Face Detection
• Image Scaling for ANN
• Face Detection in an image frame
• Object detection

Module 10 – Convolution Neural

• Convolutional Neural Networks
• CNN Architecture
• The Neuroscientific Basis for Convolutional Networks
• Convolution Process
• MaxPooling, dropout
• Maths behind CNNs
• Feature Extraction
• Variants of the Basic Convolution Function
• Efficient Convolution Algorithms
• Variety of Convolutional Networks
• Implementing CNNs using Keras
• MNIST Data – Digit Classification using CNN
• Fashion Product Identification using CNN
• CNN implementation example using Pytorch

Module 11 – Transfer Learning for Computer Vision

• Understanding the concept of Transfer Learning
• Ideas for transfer learning approach for different situations
• Overview of imagenet datasets and pre-trained models – VGG16, VGG19, ResNet50, MobileNet
• Implementing Transfer Learning-based Image Classifier using ResNet50
• Optimizing Transfer Learning-based models
• Using other pre-trained models from Tensorflow hub for Computer Vision applications
• Video Processing using Neural Networks

Module 12 – Object Localization and Object Detection

• Object Localization Overview
• Image augmentation
• Bounding box augmentation
• Object Localization using Transfer Learning
• Object Detection Overview
• Region based CNN
• Fast RCNN
• Faster RCNN
• Implementing RCNN using Tensorflow
• Introduction to SSD
• Working of SSD
• Implementation of SSD using tensorflow

Module 13 – Image Segmentation

• Image segmentation introduction
• Encoder Decoder approach for Image segmentation
• Masked RCNN
• Working with Unet – Encoder Decoder approach
• Implementation with Python

Module 14 – Capstone Project

• Working Final Project
• Splitting final Project into phases
• Working on structuring project
• Summary

Let’s talk to us if you have any query

Prerequisites:

• Basic of Networking
• Absolute Beginners. No prior AWS experience is necessary
• Interested in Deploying Applications on AWS
• Cloud Computing Enthusiasts
• AWS System Requirements- Need to set up an AWS Account(Most of the Services come with AWS free tier eligibility)
• Windows: Putty, Putty KeyGen, and Web Browser with Internet connectivity.
• Linux/Mac: Default Terminal

Certificate: Certificate of Internship

ToC for Internship & Training:

Module 1: Introduction to Cloud Computing

• Introduction to cloud computing
• Essential Characteristics of Cloud Computing
• Service Models in Cloud computing
• Deployment models in Cloud Computing
• Introduction to AWS
• AWS Account creation &free tier limitations overview

Module 2: Identity Access Management

• Root Account Vs IAM user
• Multi-Factor Authentication for Users
• IAM Password Policies
• Creating Customer Managed Policies.
• Groups
• Roles

Module 3: Glacier Storage

• What is Simple Storage Service (S3)
• Storage Classes
• Versioning
• Cross-region replication
• Life Cycle Management
• Security & Encryption
• Static Webhosting with S3 bucket
• Events configuration on S3 buckets
• Enabling cross-account access for S3
• S3 Data management and backup using 3rd Party applications.
• S3 Cross-Account Access and Pre-Signed URLs
• Storage Gateway

Module 4: Linux Introduction

• Basics of Linux for AWS
• Linux Installation and Basic commands overview
• Web Server and Services Configurations
• Compute
• EC2 Instance Launch Wizard
• EC2 Instance Types
• Generating custom Public Key and Private keys for EC2 instances
• Security groups
• Volumes and Snapshots
• Creating customized Amazon Machine Images
• RAID Overview and RAID Configurations
• User Data and Metadata
• ElasticLoad Balancers & Health Checks
• Auto Scaling Groups
• CloudWatch
• Creating Billing Alarm and EC2 instance alarms.
• AWS CLI&EC2 Roles
• Elastic File System
• AWS Lightsail
• Elastic Beanstalk
• Placement Groups

Module 5: Logging and monitoring in Amazon S3

• Amazon CloudWatch Alarms
• AWS CloudTrail Logs
• Amazon S3 Access Logs
• AWS Trusted Advisor
• Logging configuration of Amazon S3 buckets.
• Security checks for Amazon S3 buckets that have open access permissions.
• Fault tolerance checks for Amazon S3 buckets that don’t have versioning enabled, or have versioning suspended.

Module 6: Okta tool for Identity and Access Management

• Step 1: Enable provisioning in AWS SSO
• Step 2: Configure provisioning in Okta
• Step 3: Assign access for users and groups in Okta
• (Optional) Step 4: Configure user attributes in Okta for access control in AWS SSO
• (Optional) Passing attributes for access control
• Troubleshooting

Module 7: Route 53

• DNS Records overview
• Routing Policies
• Hosting sample Website and configuring Policies
• Simple Routing Policy
• Latency Routing Policy
• Failover Routing Policy
• Weighted Routing Policy
• Geolocation Routing Policy.

Module 8: Database

• Launching a RDS  Instances (MySQL, MSSQL & Aurora)
• Multi-AZ & Read Replicas for RDS instances
• DynamoDB
• Redshift
• Elastichache
• Database Migration Service and Schema conversion tool

Module 9: VPC (Virtual Private Cloud)

• Networking Basics
• Creating custom VPCs and custom Subnets
• Network ACL’s
• Route Tables & IGW
• VPC Peering
• Flow log creation
• VPN Configuration with AWS (OpenVPN)

Module 10: Security Options

• CloudTrail
• AWS Config
• Key Management Services
• AWS Certificate Manager
• AWS Inspector
• AWS Trusted Advisor
• Content Delivery Networks / CloudFront

Module 11: Application Services

• Simple Email Service
• Simple Queue Service
• Simple Workflow Service
• Simple Notification Service
• SMS – Server Migration Service
• Migrating server from on-premises to cloud
• Cloud Formation
• Directory Services and Adding EC2 instance to Domain
• AWS TCO Calculator and Simple Monthly calculator

Module 12: Project Work

• Working Final Project
• Splitting final Project into phases  Working on structuring porject
• Do’s and Don’ts with Machine Learning  Productization of Machine Learning
• Application

Let’s talk to us if you have any query

Prerequisites: Basic of Linux and Networking, any graduating candidate can join

Certificate: Certificate of Internship

ToC for Internship & Training:

Unit 1: Introduction to ethical hacking

Information security controls

Information security threats and attack vectors

• Top information security attack vectors
• Motives, goals, and objectives of information security attacks
• Information security threats
• Information warfare
• Ipv6 security threats

Hacking concepts

Hacking phases

Types of attacks

• Types of attacks on a system
• Operating system attacks
• Miss configuration attacks
• Application-level attacks

Unit 2: foot printings and reconnaissance

Foot printing concepts

• Foot printing terminology
• What is foot printing?
• Why foot printing?
• Objectives of foot printing

Foot printing threats

Website foot printing

• Mirroring entire website
• Website mirroring tools
• Monitoring web updates using website watcher

Email foot printing

• Tracking email communications
• Collecting information from email header
• Email tracking tools
• Company’s plans?

Foot printing using google

• Footprint using google hacking techniques
• What a hacker can do with google hacking?

Whois foot printing

Dns foot printing

Network foot printing

Unit 3: System Hacking

• Information at hand before system hacking stage
• System hacking: goals
• Hacking methodology (chm)
• System hacking steps
• Types of password attacks
• Offline attack: rainbow attacks
• Default passwords
• Microsoft authentication
• Password cracking tools
• Escalating privileges
• Key logger
• Spyware

Unit 4: Trojans and Backdoors

• Trojan concepts
• Trojan infection
• Types of trojans
• Trojan detection
• Anti-trojan software

Unit 5: SQL Injection

• Sql injection concepts
• Types of sql injection
• Blind sql injection
• Advanced sql injection
• Sql injection tools
• Evasion techniques
• Counter-measures

Unit 6: Penetration Testing

• Pen testing concepts
• Types of pen testing
• Pen testing techniques
• Pen testing phases
• Pen testing roadmap
• Outsourcing pen testing services

Prerequisites: None, any graduating candidate can join

Hardware Kit: Attendee can buy the hardware kit from market or from us.

Certificate: Certificate of Internship

ToC for Internship & Training:

Module 1- Introduction to IoT

• Introduction to Internet of Things
• M2M towards IoT -the global context
• Scope of IoT – Smart home, Smart Grid Applications
• IoT in India – Reality v/s Hype
• IoT Job Market
• Skills required to switch career to IoT
• Industries working on IoT
• IoT Products by Indian Companies
• Internet of Things in Indian Universities Curriculum
• Applications of IoT Electrical Engineering
• IoT Standards in Industry
• IoT Hardware Requirements
• Analysis of Arduino Uno, Arduino Yun, Raspberry Pi, Beaglebone Black, Intel Edision & Galileo

Module 2 -Hardware Layer – Arduino

• Industrial Internet of Things
• Working with Smart Grid – Analysis
• Getting started with Arduino Uno R3
• Basics of AVR MCU – RAM, Flash Memory and timers
• Arduino – Opensource Hardware Platform
• Pin Configuration and functionalities
• Getting started with Arduino IDE
• LED Interfacing with Arduino
• Introduction to Serial Communication
• PC Controlled Communication
• Introduction to basic sensors
• Sensor 1: Working & Interfacing of IR Proximity Sensor

Module 3 -Hardware Layer – Sensor Interfacing

• Sensor 2: Working & Interfacing of MQ2 Sensor
• Sensor 3: Ultrasonic Sensor Interfacing (demo)
• Sensor 4 & 5: DHT11 Interfacing, working principle
• Measuring temperature & Humidity using DHT11
• Analyzing sensor data on Serial Monitor & Serial Plotter
• Selecting a sensor for your use case
• Commercial/Industrial/Military/Medical/Food grade sensors
• Automatic Street Light Management for Smart Cities
• Understanding Wastage of Electrical Energy due to street lights mismanagement.
• Traffic Light Management Automation System

Module 4-Network Layer – Wireless Communication Protocols & Bluetooth

• IPv4 Vs IPv6
• Introduction to 6LowPAN
• IoT Physical Layer Protocols
• Getting started with HC05 – Bluetooth Module
• Connecting HC05 with Arduino
• Sensor Data Analytics using readily available Bluetooth Terminal Android Apps
• Android Controlled Device Automation with Arduino
• Working with Relay & Interfacing with Arduino
• Controlling AC Appliances with from PC – SMART Home Applications

Module 5 -Network Layer – WiFi

• Using Voice Recognition Technique
• Sending voice to text from android app via Bluetooth to arduino
• Voice controlled Device Automation
• Wifi & Lifi
• Getting Started with ESP8266-01
• Configuration, Pin Layout and Applications
• Testing AT Commands with ESP8266
• Connecting to a network
• HTTP Request Format
• Making HTTP Local Webserver using ESP8266

Module 6 -ESP8266 & Thingspeak

• Using ESP8266 as a HTTP Client
• Uploading live sensor data on thingspeak cloud using ESP8266 & GET Request
• Making a Local Webserver using Arduino
• Using Arduino as a TCP data server
• Accessing UI in a local network
• Analyzing HTTP callbacks in webserver
• Projects and Tasks
• Introduction to Transport Layer Protocols

Module 7- IoT Application Layer Protocol

• HTTP
• MQTT
• XMPP
• CoAP
• AMQP
• Websockets

Module 8- Python Programming for IoT

• Getting started with Python
• Variables, and Data Structure
• List, tuples and dictionary
• Functions in python
• Control Structure
• Object Oriented Programming
• Using Packages
• Os, time and datetime
• File Handling in Python
• Miscellaneous Functions in python

Module 9 -Python Programming

• Serial Communication in Python
• Controlling Arduino using Python
• Interfacing APIs with Python
• MQTT with Python
• Installing Paho
• Publish data to a MQTT topic using Paho
• Subscribing to an MQTT Topic using Paho

Module 10 -Raspberry Pi

• Getting Started with Raspberry Pi
• Installing OS in Rpi
• Command line and GUI Interface
• Raspbian OS Introduction & Tools
• Interfacing GPIOs with LEDs
• Interfacing sensors
• Serial Communication
• Controlling Arduino from raspberry pi

Module 11 -Raspberry Pi

• Getting started with MQTT on Raspberry Pi
• Controlling LED using Android MQTT Client
• Getting sensor data using Android MQTT Client
• Using Raspberry pi as HTTP Client to send live sensor data to thingspeak
• Installing Mosquitto on Raspberry pi
• Making pi a local MQTT broker
• Testing Publish and subscribe model on RPi
• Publishing data from PC, Android to RPi over a local network
• Controlling Pi GPIOs using iot.eclipse.org MQTT broker

Module 12 -IoT Gateway and Node-red

• Designing the IoT Gateway system
• Gathering data from multiple publishers
• Making Raspberry Pi as a IoT Gateway
• Analyzing sensor data in smartphone over internet
• Introduction to Node-red and node.js
• Getting started with node-red
• Installing dashboard, ThingSpeak node and IBM Watson node
• Basic flow in node-red
• Connecting twitter with trigger switch
• Twitting Sensor data on Twitter
• Uploading Sensor data on Thingspeak using node-red
• Uploading data to IBM Watson demo Platform using node-red
• Controlling devices from Twitter and other cloud services via node-red
• Creating front end Visualization using node-red

Module 13 -Socket Programming

• Socket programming with python
• TCP v/s UDP
• Setting up TCP server & TCP client on Raspberry Pi using python code and socket Programming
• Testing TCP client server relationship
• Setting up UDP server and UDP client on RPi using Python code and socket programming
• Testing UDP client server relationship
• CoAP Protocol (Constrained Application Protocol)
• Working of CoAP
• HTTP v/s CoAP
• Using CoAPthon library with python
• Setting up a CoAP server using CoAPthon
• Setting up CoAP client using CoAPthon

Module 14 – IoT Cloud

Module 15-Project Development

Let’s talk to us if you have any query