Generative AI Course in Delhi

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

Extracting data from various sources

Workspaces in Power BI

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

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

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

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

Why Dashboard and Dashboard vs Reports

Creating Dashboards

Conguring a Dashboard Dashboard Tiles, Pinning Tiles

Power BI Q&A

Quick Insights in Power BI

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

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

Understanding Data Refresh

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

Replacing a Dataset and Troubleshooting Refreshing

What is Machine Learning

Machine Learning Use-Cases

Machine Learning Process Flow

Machine Learning Categories

Classification and Regression

Where we use classification model and where we use regression

Regression Algorithms and its types

Logistic Regression

Evaluation Matrix of Regression 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

Hyperparameter Optimization

Grid Search vs. Random Search

Introduction to Dimensionality

Why Dimensionality Reduction

PCA

Factor Analysis

Scaling dimensional model

LDA

ICA

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

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

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

What is Time Series Analysis

Importance of TSA

Components of TSA

What is Model Selection

Need for Model Selection

Cross Validation

What is Boosting

How do Boosting Algorithms work

Types of Boosting Algorithms

Adaptive Boosting

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

Tokenization

Frequency Distribution

Different Types of Tokenizers

Bigrams, Trigrams & Ngrams

Stemming

Lemmatization

Stopwords

POS Tagging

Named Entity Recognition

Syntax Trees

Chunking

Chinking

Context Free Grammars (CFG)

Automating Text Paraphrasing

Machine Learning: Brush Up

Bag of Words

Count Vectorizer

Term Frequency (TF)

Inverse Document Frequency (IDF)

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

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

What is NN

Types of NN

Creation of simple neural network using tensorflow

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

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

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

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

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

YOLO v3

YOLO v4

Darknet

OpenVINO

ONNX

Fast R-CNN

Faster R-CNN

Mask R-CNN

What is BERT

Brief on types of BERT

Applications of BERT

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

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 Creating Programs

ChatGPT for Debugging

ChatGPT for Integrating New Features

ChatGPT for Testing

Introducing OpenAI and ChatGPT API

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

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

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

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

Introduction to DBN

Architecture of DBN

Applications of DBN

DBN in Real World

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

Introduction to SRGAN

Network Architecture - Generator, Discriminator

Loss Function - Discriminator Loss & Generator Loss

Implementation of SRGAN in Keras

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 Recognition Pipeline

Phonemes

Pre-Processing

Acoustic Model

Deep Learning Models

Decoding

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

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

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

• 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

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

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

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

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

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

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

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

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

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,

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

Public DNS

Private DNS

Routing policy

Records

Register DNS

Work with third party DNS as well

Stack

Templet

Json / Ymal

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.