Professional in Generative AI

Introduction To Python

• Installation and Working with Python
• Understanding Python variables
• Python basic Operators
• Understanding the Python blocks.

Python Keyword and Identiers

• Python Comments, Multiline Comments.
• Python Indentation
• Understating the concepts of Operators
• Arithmetic
• Relational
• Logical
• Assignment
• Membership
• Identity

Introduction To Variables

• Variables, expression condition and function
• Global and Local Variables in Python
• Packing and Unpacking Arguments
• Type Casting in Python
• Byte objects vs. string in Python
• Variable Scope

Python Data Type

• Declaring and using Numeric data types
• Using string data type and string operations
• Understanding Non-numeric data types
• Understanding the concept of Casting and Boolean.
• Strings
• List
• Tuples
• Dictionary
• Sets

Control Structure & Flow

• Statements ? if, else, elif
• How to use nested IF and Else in Python
• Loops
• Loops and Control Statements.
• Jumping Statements ? Break, Continue, pass
• Looping techniques in Python
• How to use Range function in Loop
• Programs for printing Patterns in Python
• How to use if and else with Loop
• Use of Switch Function in Loop
• Elegant way of Python Iteration
• Generator in Python
• How to use nested Loop in Python
• Use If and Else in for and While Loop
• Examples of Looping with Break and Continue Statement
• How to use IN or NOT IN keyword in Python Loop.

List

• What is List.
• List Creation
• List Length
• List Append
• List Insert
• List Remove
• List Append & Extend using ?+? and Keyword
• List Delete
• List related Keyword in Python
• List Reverse
• List Sorting
• List having Multiple Reference
• String Split to create a List
• List Indexing
• List Slicing
• List count and Looping
• List Comprehension and Nested Comprehension

Tuple

• What is Tuple
• Tuple Creation
• Accessing Elements in Tuple
• Changing a Tuple
• Tuple Deletion
• Tuple Count
• Tuple Index
• Tuple Membership
• TupleBuilt in Function (Length, Sort)

Dictionary

• Dict Creation
• Dict Access (Accessing Dict Values)
• Dict Get Method
• Dict Add or Modify Elements
• Dict Copy
• Dict From Keys.
• Dict Items
• Dict Keys (Updating, Removing and Iterating)
• Dict Values
• Dict Comprehension
• Default Dictionaries
• Ordered Dictionaries
• Looping Dictionaries
• Dict useful methods (Pop, Pop Item, Str , Update etc.)

Sets

• What is Set
• Set Creation
• Add element to a Set
• Remove elements from a Set
• PythonSet Operations
• Frozen Sets

Strings

• What is Set
• Set Creation
• Add element to a Set
• Remove elements from a Set
• PythonSet Operations

Python Function, Modules and Packages

• Python Syntax
• Function Call
• Return Statement
• Arguments in a function ? Required, Default, Positional, Variable-length
• Write an Empty Function in Python ?pass statement.
• Lamda/ Anonymous Function
• *args and **kwargs
• Help function in Python
• Scope and Life Time of Variable in Python Function
• Nested Loop in Python Function
• Recursive Function and Its Advantage and Disadvantage
• Organizing python codes using functions
• Organizing python projects into modules
• Importing own module as well as external modules
• Understanding Packages
• Random functions in python
• Programming using functions, modules & external packages
• Map, Filter and Reduce function with Lambda Function
• More example of Python Function

Decorator, Generator and Iterator

• Creation and working of decorator
• Idea and practical example of generator, use of generator
• Concept and working of Iterator

Python Exception Handling

• Python Errors and Built-in-Exceptions
• Exception handing Try, Except and Finally
• Catching Exceptions in Python
• Catching Specic Exception in Python
• Raising Exception
• Try and Finally

Python File Handling

• Opening a File
• Python File Modes
• Closing File
• Writing to a File
• Reading from a File
• Renaming and Deleting Files in Python
• Python Directory and File Management
• List Directories and Files
• Making New Directory
• Changing Directory

Memory management using python

• Threading, Multi-threading
• Memory management concept of python
• working of Multi tasking system
• Different os function with thread

Python Database Interaction

• SQL Database connection using
• Creating and searching tables
• Reading and Storing cong information on database
• Programming using database connections

Reading an excel

• Working With Excel
• Reading an excel le using Python
• Writing to an excel sheet using Python
• Python| Reading an excel le
• Python | Writing an excel le
• Adjusting Rows and Column using Python
• ArithmeticOperation in Excel le.
• Play with Workbook, Sheets and Cells in Excel using Python
• Creating and Removing Sheets
• Formatting the Excel File Data
• More example of Python Function

Complete Understanding of OS Module of Python

• Check Dirs. (exist or not)
• How to split path and extension
• How to get user prole detail
• Get the path of Desktop, Documents, Downloads etc.
• Handle the File System Organization using OS
• How to get any les and folder?s details using OS

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 Na?ve 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