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

Introduction to Large Language Models

• What is a Large Language Model

LLM Architectures

• Encoders and Decoders

• Model Ontology

• Encoders

• Decoders

• Encoders-Decoders

• Architectures at a glance

Prompting and Prompt Engineering

• Affecting the distribution over Vocabulary

• Affecting the distribution over Vocabulary

• Prompting

• Prompt Engineering

• In-context Learning and Few-shot Prompting

• Example Prompts

• Advanced Prompting Strategies

Issues with Prompting

• Prompt Injection

• Memorization

Training

• Training

• Hardware Costs

Decoding

• Decoding

• Greedy Decoding

• Non-Deterministic Decoding

• Temperature

Hallucination

• Hallucination

• Groundedness and Attributability

LLM Applications

• Retrieval Augmented Generation

• Code Models

Multi-Modal

Language Agents

OCI Generative AI Introduction

• OCI Generative AI Service

• How does OCI Generative AI service work

• Pretrained Foundational Models

• Fine-tuning

• Dedicated AI Clusters

Chat Models

• Tokens

• Pretrained Chat Models

• Chat Model Parameters

• Preamble Override

• Temperature

• Chat Model Parameters

• Top k

• Top p

• Frequency and Presence Penalties

Demo Chat Models

Demo OCI Generative AI Service Inference API

Demo Setting up OCI Config for Generative AI API

Embedding Models

• Embeddings

• Word Embeddings

Semantic Similarity

Sentence Embeddings

Embeddings use case

Embedding Models in Generative AI

Embedding Models in Generative AI

Demo Embedding Model

Customize LLMs with your data

• Training LLMs from scratch with my data

• In-context Learning / Few-shot Prompting

• Fine-tuning a pretrained model

• Fine-tuning Benefits

• Retrieval Augmented Generation (RAG)

• Customize LLMs with your data

Fine-tuning and Inference in OCI Generative AI

• Fine-tuning and Inference

• Fine-tuning workflow in OCI Generative AI

• Inference workflow in OCI Generative AI

• Dedicated AI Clusters

• T-Few Fine-tuning

T-Few fine-tuning process

Reducing Inference costs

Inference serving with minimal overhead

Dedicated AI Clusters Sizing and Pricing

• Dedicated AI Cluster Units

• Dedicated AI Cluster Units Sizing

• Dedicated AI Clusters Sizing

• Example Pricing

Demo Dedicated AI Clusters

Generative AI Fine-tuning Configuration

• Fine-tuning Configuration

• Fine-tuning Parameters (T-Few)

• Understanding Fine-tuning Results

Demo Fine-tuning and Custom Models

Demo Inference using Endpoint

OCI Generative AI Security

• Dedicated GPU and RDMA Network

• Model Endpoints

• Customer Data and Model Isolation

• Generative AI leverages OCI Security Services

Retrieval Augmented Generation

• Retrieval Augmented Generation

• RAG Framework

• RAG Techniques

• RAG Pipeline

NNX compatible

Database-Native Vector Embedding Generation

Vector Index

Vector Index Syntax

Similarity Searches in Oracle 23i

Vector Search SQL

Vector Search

AI Vector Search powers Gen AI pipelines

Application Development

Chatbot Introduction

• Chatbot Introduction

• Demo Chatbot

Chatbot Architecture & Basic Components

• Chatbot Architecture

• OCI Generative AI and LangChain Integration

• LangChain Components

Models, Prompts and Chains

• LangChain Prompt, Model and Chain Interaction

• LangChain Prompt Templates

• String Prompt Template and PromptValue

• Chat Prompt Template and PromptValue

• LangChain Models

• LangChain Models OCI Chat Models

• LangChain Models OCI Embedding Models

• LangChain Chains

• LangChain Chains

Setting Up a Development Environment

Demo Setup Development Environment

Demo Prompts, Chains, and LLMs

Extending Chatbot by Adding Memory

• LangChain Memory

• Memory

• Memory Chat Messages

• LangChain Memory Per User

• Demo Memory

• Demo Streamlitand Memory

Extending Chatbot by Adding RAG

• RAG with LangChain

• Retrieval Augmented Generation (RAG) with LangChain

• Read and Split Documents

• Embed documents and store in the vector database

• Retrieve documents and send as a context to the LLM

• Demo RAG - Indexing

• Demo RAG - Retrieval and Generation

Extending Chatbot by Adding RAG + Memory

• RAG Plus Memory

• Adding chat history as context

• Print of Response

• Demo RAG Plus Memory and Tracing with LangSmith

• Demo Evaluate Model using LangSmith

• Chatbot Technical Architecture

Deploy Chatbot to OCI Compute Instance

• Deploy Chatbot to OCI Compute Instance (Virtual Machine)

• Demo Deploy Chatbot to VM

Deploy Chatbot to OCI Data Science

• Deploy LangChain Application to Data Science as Model

Prompt Engineering Fundamentals

• Generative AI and Large Language Models

• Define Prompt Engineering: Elements of a Prompt

• Parameters of a Prompt

• Prompt Iteration and Evaluation

• Role Prompting

• Quiz: Prompt Engineering Fundamentals

Techniques of Prompting

• Zero-shot Prompting

• Few-shot Prompting

• Chain-of-Thought Prompting

• Quiz: Techniques of Prompting

• Challenge: Techniques of Prompting

• Solution: Techniques of Prompting

Examples of Prompt Engineering for Everyday Success

• Enhancing English Language Skills with Prompt Engineering

• Managing Social Media with Prompt Engineering

• Parenting Aid with Prompt Engineering

Examples of Prompt Engineering for Software Developers

• Learning to Code with Prompt Engineering

• Digital Product Creation with Prompt Engineering

• Web Development with Prompt Engineering

• SaaS Product Development with Prompt Engineering

Getting Started with ChatGPT

• Introduction to ChatGPT

• Message Types and Prompt Parameter Settings in ChatGPT

Making Professional Cover Letters with ChatGPT

• The Basics of Cover Letters

• Writing Cover Letters with ChatGPT

• Cover Letters for Different Experience Levels

• Cover Letters for Different Industries and Job Roles

• Summary: Cover Letters

Building Professional Resumes with ChatGPT

• The Basics of Resumes

• Creating a Resume with ChatGPT

• Updating a Resume with ChatGPT

• Resume Formatting

• Case Studies

• Summary: Resumes

Writing Professional Emails with ChatGPT

• A Simple Email

• Emails for Different Scenarios

• Responding to Emails

• Summary: Emails

Optimize Your Linked In Profile with ChatGPT

• Basics of a LinkedIn Profile

• Optimizing Your Profile

• Creating a Job-Specific Profile

• Summary: LinkedIn Profile

Exploring Job Search Strategies with ChatGPT

• Finding Jobs by Interest and Skills

• Researching Companies and Job Titles

• Preparing for Interviews

• Summary: Job Search Strategies

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 hosting 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)
• Amazon EBS
• Demo of AMI Creation
• Backup, Restore
• Exercise
• Mock
• 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
• Mount Point with S3
• 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
• SQS
• Cloud Watch with Agent
• Cloud Watch with System Manager

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
• Path Based load balancer
• Hands on with scenario based
• Routing policy on Load balancer

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
• Endpoint Creation with VPC

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,
• SQS

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 Backup and DR Setup
• AWS High Availability Design
• AWS Best Practices (Cost +Security)
• AWS Calculator & Consolidated Billing

Migrating to Cloud & AWS

• In this module, you will learn how to migrate to cloud.
• Migration to Cloud
• Migration to AWS
• Step-by-step process

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

• ECS, EKS (Kubernetes), Docker
• Comprehensive hands-on with Dockers & Kubernetes Components
• Docker & Kubernetes Architecture & Components and installation
• Get introduced to deploy stateful and stateless apps on the cluster
• Learn how to expose the app outside the cluster and to auto-scale apps
• Expertise in learning with use cases of Containers and Docker

• Linux
• Installation of Linux
• Configuration
• Manage
• Installation of app on Linux (apache / Nginx etc)
• AWS cli configuration on Linux
• Complete hands-on on Linux.

• Python

• Boto

• DMS

• System Manager

• Mock

• Interview preparation

• Scenario-based lab and practical

• Each topic and service will be covered with lab and theory.

• Security: KMS / SSM/ WAF

• Storage: EFS, NFS, FSX, Storage Gateway

Manage Azure identities and governance (15-20%)

• Manage Azure AD objects
• create users and groups
• manage user and group properties
• manage device settings
• perform bulk user updates
• manage guest accounts
• configure Azure AD Join
• configure self-service password reset
• NOTE: Azure AD Connect; PIM

• Manage role-based access control (RBAC)
• create a custom role
• provide access to Azure resources by assigning roles
• subscriptions
• resource groups
• resources (VM, disk, etc.)
• interpret access assignments
• manage multiple directories

• Manage subscriptions and governance
• configure Azure policies
• configure resource locks
• apply tags
• create and manage resource groups
• move resources
• remove RGs
• manage subscriptions
• configure Cost Management
• configure management groups

Implement and Manage Storage (10-15%)

• Manage storage accounts
• configure network access to storage accounts
• create and configure storage accounts
• generate shared access signature
• manage access keys
• implement Azure storage replication
• configure Azure AD Authentication for a storage account

• Manage data in Azure Storage
• export from Azure job
• import into Azure job
• install and use Azure Storage Explorer
• copy data by using AZ Copy

• Configure Azure files and Azure blob storage
• create an Azure file share
• create and configure Azure File Sync service
• configure Azure blob storage
• configure storage tiers for Azure blobs

Deploy and Manage Azure Compute Resources (25-30%)

• Configure VMs for high availability and scalability
• configure high availability
• deploy and configure scale sets

• Automate deployment and configuration of VMs
• modify Azure Resource Manager (ARM) template
• configure VHD template
• deploy from template
• save a deployment as an ARM template
• automate configuration management by using custom script extensions

• Create and configure VMs
• configure Azure Disk Encryption
• move VMs from one resource group to another
• manage VM sizes
• add data discs
• configure networking
• redeploy VMs

• Create and configure containers
• create and configure Azure Kubernetes Service (AKS)
• create and configure Azure Container Instances (ACI)
• NOT: selecting a container solution architecture or product; container registry settings

• Create and configure Web Apps
• create and configure App Service
• create and configure App Service Plans
• NOT: Azure Functions; Logic Apps; Event Grid

Configure and Manage Virtual Networking (30-35%)

• Implement and manage virtual networking
• create and configure VNET peering
• configure private and public IP addresses, network routes, network interface, subnets, and virtual network

• Configure name resolution
• configure Azure DNS
• configure custom DNS settings
• configure a private or public DNS zone

• Secure access to virtual networks
• create security rules
• associate an NSG to a subnet or network interface
• evaluate effective security rules
• deploy and configure Azure Firewall
• deploy and configure Azure Bastion Service
• NOT: Implement Application Security Groups; DDoS

• Configure load balancing
• configure Application Gateway
• configure an internal load balancer
• configure load balancing rules
• configure a public load balancer
• troubleshoot load balancing
• NOT: Traffic Manager and Front Door and Private Link

• Monitor and troubleshoot virtual networking
• monitor on-premises connectivity
• use Network Performance Monitor
• use Network Watcher
• troubleshoot external networking
• troubleshoot virtual network connectivity

• Integrate an on-premises network with an Azure virtual network
• create and configure Azure VPN Gateway
• create and configure VPNs
• configure ExpressRoute
• configure Azure Virtual WAN

Monitor and Back up Azure Resources (10-15%)

• Monitor resources by using Azure Monitor
• configure and interpret metrics
• analyze metrics across subscriptions
• configure Log Analytics
• implement a Log Analytics workspace
• configure diagnostic settings
• query and analyze logs
• create a query
• save a query to the dashboard
• interpret graphs
• set up alerts and actions
• create and test alerts
• create action groups
• view alerts in Azure Monitor
• analyze alerts across subscriptions
• configure Application Insights
• NOT: Network monitoring

• Implement backup and recovery
• configure and review backup reports
• perform backup and restore operations by using Azure Backup Service
• create a Recovery Services Vault
• use soft deletes to recover Azure VMs
• create and configure backup policy
• perform site-to-site recovery by using Azure Site Recovery
• NOT: SQL or HANA