Generative AI Course in Gurgaon

Introduction to Power BI

• Overview of BI concepts

• Why we need BI

• Introduction to SSBI

• SSBI Tools

• Why Power BI

• What is Power BI

• Building Blocks of Power BI

• Getting started with Power BI Desktop

• Get Power BI Tools

• Introduction to Tools and Terminology

• Dashboard in Minutes

• Interacting with your Dashboards

• Sharing Dashboards and Reports

Power BI Desktop

• Power BI Desktop

• Extracting data from various sources

• Workspaces in Power BI

Power BI Data Transformation

• Data Transformation

• Query Editor

• Connecting Power BI Desktop to our Data Sources

• Editing Rows

• Understanding Append Queries

• Editing Columns

• Replacing Values

• Formatting Data

• Pivoting and Unpivoting Columns

• Splitting Columns

• Creating a New Group for our Queries

• Introducing the Star Schema

• Duplicating and Referencing Queries

• Creating the Dimension Tables

• Entering Data Manually

• Merging Queries

• Finishing the Dimension Table

• Introducing the another DimensionTable

• Creating an Index Column

• Duplicating Columns and Extracting Information

• Creating Conditional Columns

• Creating the FACT Table

• Performing Basic Mathematical Operations

• Improving Performance and Loading Data into the Data Model

Modelling with Power BI

• Introduction to Modelling

• Modelling Data

• Manage Data Relationship

• Optimize Data Models

• Cardinality and Cross Filtering

• Default Summarization & Sort by

• Creating Calculated Columns

• Creating Measures & Quick Measures

Data Analysis Expressions (DAX)

• What is DAX

• Data Types in DAX

• Calculation Types

• Syntax, Functions, Context Options

• DAX Functions

• Date and Time

• Time Intelligence

• Information

• Logical

• Mathematical

• Statistical

• Text and Aggregate

• Measures in DAX

• Measures and Calculated Columns

• ROW Context and Filter Context in DAX

• Operators in DAX - Real-time Usage

• Quick Measures in DAX - Auto validations

• In-Memory Processing DAX Performance

Power BI Desktop Visualisations

• How to use Visual in Power BI

• What Are Custom Visuals

• Creating Visualisations and Colour Formatting

• Setting Sort Order

• Scatter & Bubble Charts & Play Axis

• Tooltips and Slicers, Timeline Slicers & Sync Slicers

• Cross Filtering and Highlighting

• Visual, Page and Report Level Filters

• Drill Down/Up

• Hierarchies and Reference/Constant Lines

• Tables, Matrices & Conditional Formatting

• KPI's, Cards & Gauges

• Map Visualizations

• Custom Visuals

• Managing and Arranging

• Drill through and Custom Report Themes

• Grouping and Binning and Selection Pane, Bookmarks & Buttons

• Data Binding and Power BI Report Server

Introduction to Power BI Dashboard and Data Insights

• Why Dashboard and Dashboard vs Reports

• Creating Dashboards

• Conguring a Dashboard Dashboard Tiles, Pinning Tiles

• Power BI Q&A

• Quick Insights in Power BI

Direct Connectivity

• Custom Data Gateways

• Exploring live connections to data with Power BI

• Connecting directly to SQL Server

• Connectivity with CSV & Text Files

• Excel with Power BI Connect Excel to Power BI, Power BI Publisher for Excel

• Content packs

• Update content packs

Publishing and Sharing

• Introduction and Sharing Options Overview

• Publish from Power BI Desktop and Publish to Web

• Share Dashboard with Power BI Service

• Workspaces (Power BI Pro) and Content Packs (Power BI Pro)

• Print or Save as PDF and Row Level Security (Power BI Pro)

• Export Data from a Visualization

• Export to PowerPoint and Sharing Options Summary

Refreshing Datasets

• Understanding Data Refresh

• Personal Gateway (Power BI Pro and 64-bit Windows)

• Replacing a Dataset and Troubleshooting Refreshing

Introduction to Machine Learning

• What is Machine Learning

• Machine Learning Use-Cases

• Machine Learning Process Flow

• Machine Learning Categories

Supervised Learning

• Classification and Regression

• Where we use classification model and where we use regression

• Regression Algorithms and its types

Regression Algorithm

• Logistic Regression

• Evaluation Matrix of Regression Algorithm

Classification Algorithm

• Implementing KNN

• Implementing Nave Bayes Classifier

• Implementation and Introduction to Decision Tree using CARTand ID3

• Introduction to Ensemble Learning

• Random Forest algorithm using bagging and boosting

• Evaluation Matrix of classification algorithms (confusion matrix, r2score, Accuracy,f1-score,recall and precision

Optimization Algorithm

• Hyperparameter Optimization

• Grid Search vs. Random Search

Dimensionality Reduction

• Introduction to Dimensionality

• Why Dimensionality Reduction

• PCA

• Factor Analysis

• Scaling dimensional model

• LDA

• ICA

Unsupervised Learning

• What is Clustering & its Use Cases

• What is K-means Clustering

• How does the K-means algorithm works

• How to do optimal clustering

• What is Hierarchical Clustering

• How does Hierarchical Clustering work

Association Rules Mining and Recommendation Systems

• What are Association Rules

• Association Rule Parameters

• Calculating Association Rule Parameters

• Recommendation Engines

• How do Recommendation Engines work

• Collaborative Filtering

• Content-Based Filtering

• Association Algorithms

• Implementation of Apriori Association Algorithm

Reinforcement Learning

• What is Reinforcement Learning

• Why Reinforcement Learning

• Elements of Reinforcement Learning

• Exploration vs. Exploitation dilemma

• Epsilon Greedy Algorithm

• Markov Decision Process (MDP)

• Q values and V values

• Q Learning

• Values

Time Series Analysis

• What is Time Series Analysis

• Importance of TSA

• Components of TSA

Model Selection and Boosting

• What is Model Selection

• Need for Model Selection

• Cross Validation

• What is Boosting

• How do Boosting Algorithms work

• Types of Boosting Algorithms

• Adaptive Boosting

Introduction to Text Mining and NLP

• Overview of Text Mining

• Need of Text Mining

• Natural Language Processing (NLP) in Text Mining

• Applications of Text Mining

• OS Module

• Reading, Writing to text and word files

• Setting the NLTK Environment

• Accessing the NLTK Corpora

Extracting, Cleaning and Preprocessing Text

• Tokenization

• Frequency Distribution

• Different Types of Tokenizers

• Bigrams, Trigrams & Ngrams

• Stemming

• Lemmatization

• Stopwords

• POS Tagging

• Named Entity Recognition

Analyzing Sentence Structure

• Syntax Trees

• Chunking

• Chinking

• Context Free Grammars (CFG)

• Automating Text Paraphrasing

Text Classification - I

• Machine Learning: Brush Up

• Bag of Words

• Count Vectorizer

• Term Frequency (TF)

• Inverse Document Frequency (IDF)

Getting Started with TensorFlow 2.0

• Introduction to TensorFlow 2.x

• Installing TensorFlow 2.x

• Defining Sequence model layers

• Activation Function

• Layer Types

• Model Compilation

• Model Optimizer

• Model Loss Function

• Model Training

• Digit Classification using Simple Neural Network in TensorFlow 2.x

• Improving the model

• Adding Hidden Layer

• Adding Dropout

• Using Adam Optimizer

Introduction to Deep Learning

• What is Deep Learning

• Curse of Dimensionality

• Machine Learning vs. Deep Learning

• Use cases of Deep Learning

• Human Brain vs. Neural Network

• What is Perceptron

• Learning Rate

• Epoch

• Batch Size

• Activation Function

• Single Layer Perceptron

Neural Networks

• What is NN

• Types of NN

• Creation of simple neural network using tensorflow

Convolution Neural Network

• Image Classification Example

• What is Convolution

• Convolutional Layer Network

• Convolutional Layer

• Filtering

• ReLU Layer

• Pooling

• Data Flattening

• Fully Connected Layer

• Predicting a cat or a dog

• Saving and Loading a Model

• Face Detection using OpenCV

Image Processing and Computer Vision

• Introduction to Vision

• Importance of Image Processing

• Image Processing Challenges Interclass Variation, ViewPoint Variation, Illumination, Background Clutter, Occlusion & Number of Large Categories

• Introduction to Image Image Transformation, Image Processing Operations & Simple Point Operations

• Noise Reduction Moving Average & 2D Moving Average

• Image Filtering Linear & Gaussian Filtering

• Disadvantage of Correlation Filter

• Introduction to Convolution

• Boundary Effects Zero, Wrap, Clamp & Mirror

• Image Sharpening

• Template Matching

• Edge Detection Image filtering, Origin of Edges, Edges in images as Functions, Sobel Edge Detector

• Effect of Noise

• Laplacian Filter

• Smoothing with Gaussian

• LOG Filter Blob Detection

• Noise Reduction using Salt & Pepper Noise using Gaussian Filter

• Nonlinear Filters

• Bilateral Filters

• Canny Edge Detector - Non Maximum Suppression, Hysteresis Thresholding

• Image Sampling & Interpolation Image Sub Sampling, Image Aliasing, Nyquist Limit, Wagon Wheel Effect, Down Sampling with Gaussian Filter, Image Pyramid, Image Up Sampling

• Image Interpolation Nearest Neighbour Interpolation, Linear Interpolation, Bilinear Interpolation & Cubic Interpolation

• Introduction to the dnn module
• Deep Learning Deployment Toolkit
• Use of DLDT with OpenCV4.0

• OpenVINO Toolkit
• Introduction
• Model Optimization of pre-trained models
• Inference Engine and Deployment process

Regional CNN

• Regional-CNN

• Selective Search Algorithm

• Bounding Box Regression

• SVM in RCNN

• Pre-trained Model

• Model Accuracy

• Model Inference Time

• Model Size Comparison

• Transfer Learning

• Object Detection Evaluation

• mAP

• IoU

• RCNN Speed Bottleneck

• Fast R-CNN

• RoI Pooling

• Fast R-CNN Speed Bottleneck

• Faster R-CNN

• Feature Pyramid Network (FPN)

• Regional Proposal Network (RPN)

• Mask R-CNN

Introduction to RNN and GRU

• Issues with Feed Forward Network

• Recurrent Neural Network (RNN)

• Architecture of RNN

• Calculation in RNN

• Backpropagation and Loss calculation

• Applications of RNN

• Vanishing Gradient

• Exploding Gradient

• What is GRU

• Components of GRU

• Update gate

• Reset gate

• Current memory content

• Final memory at current time step

RNN, LSTM

• What is LSTM

• Structure of LSTM

• Forget Gate

• Input Gate

• Output Gate

• LSTM architecture

• Types of Sequence-Based Model

• Sequence Prediction

• Sequence Classification

• Sequence Generation

• Types of LSTM

• Vanilla LSTM

• Stacked LSTM

• CNN LSTM

• Bidirectional LSTM

• How to increase the efficiency of the model

• Backpropagation through time

• Workflow of BPTT

Faster Object Detection Algorithm

• YOLO v3

• YOLO v4

• Darknet

• OpenVINO

• ONNX

• Fast R-CNN

• Faster R-CNN

• Mask R-CNN

BERT Algorithm

• What is BERT

• Brief on types of BERT

• Applications of BERT

Understanding ChatGPT

• Introduction to Generative AI

• Introduction to ChatGPT and OpenAI

• Unleashing the Power of ChatGPT

• The Applications of ChatGPT

• Human-AI Collaboration and the Future

• Engaging with ChatGPT

• Wrapping Up and Looking Ahead

ChatGPT for Productivity

• Leveraging ChatGPT for Productivity

• Mastering Excel through ChatGPT

• Becoming a Data Scientist using ChatGPT

• Data Analysis in PowerBI with ChatGPT

• Creating a Content Marketing Plan

• Social Media Marketing using ChatGPT

• Keyword Search and SEO using ChatGPT

• Generating Content using ChatGPT

• Implementing ChatGPT for Customer Service

• Email Marketing using ChatGPT

• Developing a Project Management Plan using ChatGPT

ChatGPT for Developers and Exploring ChatGPT API

• ChatGPT for Creating Programs

• ChatGPT for Debugging

• ChatGPT for Integrating New Features

• ChatGPT for Testing

• Introducing OpenAI and ChatGPT API

Developing Web Application using ChatGPT

• Building web development architecture

• Building backend server

• Setting up the database

• Setting up a React-based client-side application

• Writing user API requests to MongoDB with Express and React

• Fetching and updating the database with MongoDB API and routing with Express

• Routing to React-based client-side application

• Debugging and client-side coding

• Building a BMI Calculation application

• Building a website and create landing page content using ChatGPT

GPT models

• Transformers (Encoder - Decoder Model by doing away from RNN variants)

• Bidirectional Encoder Representation from Transformer (BERT)

• OpenAI GPT-2 & GPT-3 Models (Generative Pre-Training)

• Text Summarization with T5

• Configurations of BERT

• Pre-Training the BERT Model

• ALBERT, RoBERTa, ELECTRA, SpanBERT, DistilBERT, TinyBERT

Deep RNN and Deep LSTM

• Introduction to LSTM Architecture

• Importance of Cell State, Input Gate, Output Gate, Forget Gate, Sigmoid and Tanh

• Mathematical Calculations to Process Data in LSTM

• RNN vs LSTM - Bidirectional vs Deep Bidirectional RNN

• Deep RNN vs Deep LSTM

Autoncoders

• Autoencoders
• Intuition
• Comparison with other Encoders (MP3 and JPEG)
• Implementation in Keras

• Deep AutoEncoders
• Intuition
• Implementing DAE in Keras

• Convolutional Autoencoders
• Intuition
• Implementation in Keras

• Variational Autoencoders
• IntuitionImplementation in Keras

• Introduction to Restricted Boltzmann Machines - Energy Function, Schematic implementation, Implementation in TensorFlow

DBN Architecture

• Introduction to DBN

• Architecture of DBN

• Applications of DBN

• DBN in Real World

Generative Adversarial Networks GAN

• Introduction to Generative Adversarial Networks (GANS)

• Data Analysis and Pre-Processing

• Building Model

• Model Inputs and Hyperparameters

• Model losses

• Implementation of GANs

• Defining the Generator and Discriminator

• Generator Samples from Training

• Model Optimizer

• Discriminator and Generator Losses

• Sampling from the Generator

• Advanced Applications of GANS
• Pix2pixHD
• CycleGAN
• StackGAN++ (Generation of photo-realistic images)
• GANs for 3D data synthesis
• Speech quality enhancement with SEGAN

SRGAN

• Introduction to SRGAN

• Network Architecture - Generator, Discriminator

• Loss Function - Discriminator Loss & Generator Loss

• Implementation of SRGAN in Keras

Q-Learning Type of Reinforcement Learning

• Reinforcement Learning

• Deep Reinforcement Learning vs Atari Games

• Maximizing Future Rewards

• Policy vs Values Learning

• Balancing Exploration With Exploitation

• Experience Replay, or the Value of Experience

• Q-Learning and Deep Q-Network as a Q-Function

• Improving and Moving Beyond DQN

• Keras Deep Q-Network

Speech to Text Building

• Speech Recognition Pipeline

• Phonemes

• Pre-Processing

• Acoustic Model

• Deep Learning Models

• Decoding

Chatbot Building

• Introduction to Chatbot

• NLP Implementation in Chatbot

• Integrating and implementing Neural Networks Chatbot

• Introduction to Sequence to Sequence models and Attention
• Transformers and it applications
• Transformers language models
• BERT
• Transformer-XL (pretrained model: transfo-xl-wt103)
• XLNet

• Building a Retrieval Based Chatbot

• Deploying Chatbot in Various Platforms

Auto ML

• AutoML Methods
• Meta-Learning
• Hyperparameter Optimization
• Neural Architecture Search
• Network Architecture Search

• AutoML Systems
• MLBox
• Auto-Net 1.0 & 2.0
• Hyperas

• AutoML on Cloud - AWS
• Amazon SageMaker
• Sagemaker Notebook Instance for Model Development, Training and Deployment
• XG Boost Classification Model
• Training Jobs
• Hyperparameter Tuning Jobs

• AutoML on Cloud - Azure
• Workspace
• Environment
• Compute Instance
• Compute Targets
• Automatic Featurization
• AutoML and ONNX

Explainable AI

• Introduction to XAI - Explainable Artificial Intelligence

• Why do we need it

• Levels of Explainability
• Direct Explainability
• Simulatability
• Decomposability
• Algorithmic Transparency
• Post-hoc Explainability
• Model-Agnostic Algorithms
• Explanation by simplification (Local Interpretable Model-Agnostic Explanations (LIME))
• Feature relevance explanation
• SHAP
• QII
• SA
• ASTRID
• XAI
• Visual Explanations

• General AI vs Symbolic Al vs Deep Learning

Generative AI Prompt Engineering and LLM

• Generative AI
• Creative Applications
• Data Augmentation

• Diffusion Models
• Realistic Data Generation
• Applications Beyond Text
• LLM
• Prompt Engineering
• Fine-Tuning for Specific Tasks
• Mitigating Bias and Ethical Concerns
• Tailoring to Domain-Specific Contexts
• Building AI application with Gradio
• Large language Model with Semantic Search
• Pair Programming with Large Language Model
• Understanding and Applying Text Embedding
• LLMOps Vs DevOps

Introduction to Cloud Computing

• In this module, you will learn what Cloud Computing is and what are the different models of Cloud Computing along with the key differentiators of different models. We will also introduce you to virtual world of AWS along with AWS key vocabulary, services and concepts.
• A Short history
• Client Server Computing Concepts
• Challenges with Distributed Computing
• Introduction to Cloud Computing
• Why Cloud Computing
• Benefits of Cloud Computing

Amazon EC2 and Amazon EBS

• In this module, you will learn about the introduction to compute offering from AWS called EC2. We will cover different instance types and Amazon AMIs. A demo on launching an AWS EC2 instance, connect with an instance and host ing a website on AWS EC2 instance. We will also cover EBS storage Architecture (AWS persistent storage) and the concepts of AMI and snapshots.
• Amazon EC2
• EC2 Pricing
• EC2 Type
• Installation of Web server and manage like (Apache/ Nginx)
• Demo of AMI Creation
• Exercise
• Hands on both Linux and Windows

Amazon Storage Services S3 (Simple Storage Services)

• In this module, you will learn how AWS provides various kinds of scalable storage services. In this module, we will cover different storage services like S3, Glacier, Versioning, and learn how to host a static website on AWS.
• Versioning
• Static website
• Policy
• Permission
• Cross region Replication
• AWS-CLI
• Life cycle
• Classes of Storage
• AWS CloudFront
• Real scenario Practical
• Hands-on all above

Cloud Watch & SNS

• In this module, you will learn how to monitoring AWS resources and setting up alerts and notifications for AWS resources and AWS usage billing with AWS CloudWatch and SNS.
• Amazon Cloud Watch
• SNS - Simple Notification Services
• Cloud Watch with Agent

Scaling and Load Distribution in AWS

• In this module, you will learn about 'Scaling' and 'Load distribution techniques' in AWS. This module also includes a demo of Load distribution & Scaling your resources horizontally based on time or activity.
• Amazon Auto Scaling
• Auto scaling policy with real scenario based
• Type of Load Balancer
• Hands on with scenario based

AWS VPC

• In this module, you will learn introduction to Amazon Virtual Private Cloud. We will cover how you can make public and private subnet with AWS VPC. A demo on creating VPC. We will also cover overview of AWS Route 53.
• Amazon VPC with subnets
• Gateways
• Route Tables
• Subnet
• Cross region Peering

Identity and Access Management Techniques (IAM)

• In this module, you will learn how to achieve distribution of access control with AWS using IAM.

• Amazon IAM
• add users to groups,
• manage passwords,
• log in with IAM-created users.

• User

• Group

• Role

• Policy

Amazon Relational Database Service (RDS)

• In this module, you will learn how to manage relational database service of AWS called RDS.
• Amazon RDS
• Type of RDS
• RDS Failover
• RDS Subnet
• RDS Migration
• Dynamo DB (No SQL DB)
• Redshift Cluster
• SQL workbench
• JDBC / ODBC

Multiple AWS Services and Managing the Resources' Lifecycle

• In this module, you will get an overview of multiple AWS services. We will talk about how do you manage life cycle of AWS resources and follow the DevOps model in AWS. We will also talk about notification and email service of AWS along with Content Distribution Service in this module.
• Cloud Trail,

AWS Architecture and Design

• In this module, you will cover various architecture and design aspects of AWS. We will also cover the cost planning and optimization techniques along with AWS security best practices, High Availability (HA) and Disaster Recovery (DR) in AWS.
• AWS High Availability Design
• AWS Best Practices (Cost +Security)
• AWS Calculator & Consolidated Billing

Migrating to Cloud & AWS

Router S3 DNS

• Public DNS

• Private DNS

• Routing policy

• Records

• Register DNS

• Work with third party DNS as well

Cloud Formation

• Stack

• Templet

• Json / Ymal

Elastic Beanstalk

EFS / NFS (hands-on practice)

Hands-on practice on various Topics

Linux

• Installation of Linux

• Configuration

• Manage

• Installation of app on Linux (apache / Nginx etc)

• AWS cli configuration on Linux

• Complete hands-on on Linux.

• Scenario based lab and practical

• Each topic and services will be cover with lab and theory.