Generative AI – Masterclass

Generative AI is a subset of deep learning.  It uses AI neural networks and can process both labelled and unlabelled data using supervised, unsupervised, and semi-supervised methods. It refers to a class of artificial intelligence models and algorithms designed to create new content. These models can generate text, images, music, and other forms of data that mimic human-created content. Generative AI applications are built on top of large language models (LLMs) and foundation models. LLMs are deep learning models.
LLMs are a subset of deep learning. LLMs are AI models that power chatbots, such as ChatGPT, Copilot, Google Gemini, etc. LLMs refer to large, general-purpose language models that can be pre-trained and then fine-tuned for specific purposes.

Artificial Intelligence vs Data Science vs Machine Learning vs Deep Learning

Deep Learning Types

AI Introduction

What is Generative AI

Techniques for implementing Generative AI

Section C: What are Transformer Models

Generative AI – Transformers

Generative AI and its Techniques

Large Language Models (LLMs) and its use cases

Large Language Models

Generative AI - Applications & Challenges

Generative AI - Chatbots (Model Types)

Generative AI - Features & Examples

More About Generative AI

What are Prompts

Popular AI Chatbots

Prompts and AI Chatbots

What is a Foundation Model

Types of Foundation Model

RAG Technique

Generative Al Framework - LangChain

Generative Al Framework - Llamalndex

Build and Deploy Al Models

Run LLMs locally

Ollama

LM Studio

Optimize LLMs

Terminologies

Al- Coding Assistant

Al-Image Generation Models

Al Video Generation Models

Al -Ethics

Generative AI – Advanced Concepts

Get a brief overview of Gen AI and its models, features, LLMs, Transformer Models, etc.

Python for Cyber Security: Scripting for Hackers & Analysts

This course is tailored for security professionals and students who want to use Python to simplify and automate security operations. From automating reconnaissance to writing custom sniffers, students will engage in practical projects relevant to real-world cybersecurity roles.

Introduction to Python for Security

Automating Nmap with Subprocess and Parsing

Building file Monitoring Scripts

Credential Harvesting Simulation

Using Scapy for Packet Sniffing and Analysis

Creating simple CLI Security Tools

Learn to use Python to automate cybersecurity tasks like scanning, data parsing, and packet sniffing for real-world use.

Ethical Hacking Basics and Lab Setup for Beginners

This course is perfect for beginners looking to enter the field of ethical hacking. It introduces the core principles of hacking and guides learners step-by-step to set up their virtual hacking lab. Participants will gain a strong foundation in hacking methodology, tools, and responsible practices, preparing them for advanced penetration testing courses.

Introduction to Ethical Hacking

Overview of Hacker Types and Hacking Phases

Virtualization (VirtualBox, VMware)

Installing Kali Linux and Vulnerable Targets

Basic Linux Commands for Hackers

Lab Safety, Legal Considerations, and Next Steps

Start your ethical hacking journey with foundational knowledge and build your own hacking lab using free tools.

The Evolution of AI from Classical to Generative

This course introduces students to the key advancements that define the evolution of AI, from classical algorithms to modern generative systems. With a focus on transformer architecture, attention mechanisms, similarity measures, and neural representations, the course demystifies how models like GPT function. It addresses both technical concepts and societal implications, including energy efficiency, regulatory concerns, agentic AI systems, and shadow AI. Through case studies and current trends, students will gain a working understanding of multi-modal models, local fine-tuning techniques like LoRA, and applications such as Retrieval-Augmented Generation (RAG). The course emphasizes both conceptual clarity and practical awareness to prepare students for the fast-changing AI landscape.

Classical AI vs. Generative AI

Transformer architecture: Encoder, Decoder, Attention

Introduction to Generative AI

Dot product, Cosine similarity, and Scaled dot product

Word context and semantic similarity

Self-attention in language models

Understanding Attention Mechanisms

From chatbot hype to practical tool integration

Multimodal AI (text, image, video)

Smaller model strategies (e.g., Mistral, MoE models)

Current Trends in Generative AI

GPU and cloud cost implications

Techniques: Quantization, LoRA (Low-Rank Adaptation)

Rank decomposition and matrix simplification

Infrastructure and Optimization

Retrieval-Augmented Generation: Purpose and process

Advantages for enterprise and context-specific models

Custom AI with RAG

Compound AI systems: LLMs + LAMs

AI governance and regulatory frameworks

The rise of Shadow AI in institutions

Future of AI: Agentic and Regulated

Narrow AI vs. Broad AI

Artificial General Intelligence (AGI)

Artificial Super Intelligence (ASI)

AI Capability Spectrum

This course explores the transformative journey of Artificial Intelligence from classical prediction systems to cutting-edge generative models. Students will learn how technologies such as attention mechanisms, cosine similarity, and retrieval-augmented generation (RAG) enable today’s AI capabilities across text, image, and video.

Linux Fundamentals for Aspiring Cyber Security Professionals

Linux is the core operating system used in cybersecurity environments. This course offers foundational Linux knowledge tailored for cybersecurity use cases. Learners will explore command-line tools, scripting, user management, and more, all with a hands-on approach. No prior experience with Linux is required.

Introduction to Linux and Distributions (Ubuntu, Kali)

Terminal vs GUI

File and Directory Management

User and Group Management

File Permissions and Sudo

Introduction to Bash Scripting

Useful Tools (grep, chmod, netstat, etc.)

Master the Linux environment to kickstart your cybersecurity journey, command-line, scripting, permissions, and more.

Machine Learning Explained

This course provides a comprehensive, beginner-friendly introduction to the field of machine learning and deep learning. Starting with the basics of supervised, unsupervised, and reinforcement learning, students learn how models are trained, optimized, and deployed. Core algorithms like decision trees, logistic regression, and neural networks are explained through real-life analogies and case studies. The course also covers deep learning fundamentals including gradient descent, backpropagation, activation functions, and multilayer perceptrons (MLPs). Toward the end, students explore state-of-the-art frameworks such as TensorFlow, PyTorch, and SystemML, gaining insights into how deep learning applications are built in industry today.
This course demystifies the core principles of machine learning and deep learning through engaging visual examples and conceptual clarity. It guides students from the foundations of supervised, unsupervised, and reinforcement learning to neural network training, decision trees, and modern deep learning ecosystems.

Deterministic vs. probabilistic systems

Optimization and prediction problems

Supervised, unsupervised, and reinforcement learning paradigms

Introduction to Machine Learning

Logistic and linear regression

Decision tree classifiers

Support Vector Machines, K-Means, Naïve Bayes

Core Machine Learning Algorithms

Structure of neural networks (input, hidden, output layers)

Activation functions (sigmoid, tanh)

Training neural networks via gradient descent and backpropagation

Role of weights, biases, and cost functions

Neural Networks and Deep Learning

Gradient descent learning rate and error rate

Common pitfalls: local minima, overfitting

Techniques: momentum, dynamic learning rates, stochastic sampling

Model Training Techniques

Multilayer Perceptrons (MLPs)

Bias nodes and network topology

Hidden layer behavior and representation learning

Advanced Neural Architectures

Overview of leading frameworks: TensorFlow, PyTorch, Theano, Apache SystemML, Caffe, CNTK

Platform-as-a-Service (PaaS) providers and their integration in AI projects

Considerations for real-world applications and time-series modeling (e.g., LSTM)

Deep Learning Ecosystem

Machine Learning (ML) is AI that enables systems to learn from data, improving accuracy without explicit programming. Used in predictions, automation & more.

AWS DevOps CI/CD – 6-Hour Hands-On Crash Course

This accelerated 6-hour course provides a practical introduction to implementing DevOps practices using AWS services. Focused on Continuous Integration and Continuous Deployment (CI/CD), the course equips learners with the knowledge and tools to automate software delivery, improve deployment speed, and manage infrastructure efficiently using AWS DevOps services. Learners will get hands-on exposure to tools like AWS CodePipeline, CodeBuild, CodeDeploy, and integrations with GitHub.

What is DevOps?

CI/CD concepts and benefits

AWS DevOps toolchain overview

Introduction to DevOps and CI/CD

Overview of Git & GitHub

Introduction to AWS CodeCommit

Connecting GitHub with AWS services

Setting Up Version Control

Introduction to AWS CodeBuild

Creating a buildspec.yml file

Automating unit and integration tests

Build and Test Automation

Overview of AWS CodeDeploy

Deployment to EC2, Lambda, or ECS

Creating appspec.yml and deployment configurations

Deployment Automation with CodeDeploy

Step-by-step setup of AWS CodePipeline

Integrating CodeCommit/GitHub, CodeBuild, and CodeDeploy

Adding approval stages and notifications

Building a CI/CD Pipeline with CodePipeline

CloudWatch logs and metrics

Rollback strategies and error handling

Security, IAM roles, and least privilege principles

Monitoring, Logging, and Best Practices

Build and deploy a sample app using the full CI/CD pipeline

Final Q&A and recap of DevOps best practices on AWS

Final Project & Review

Master AWS DevOps in just 6 hours! Learn CI/CD tools and workflows through hands-on projects to build, test, and deploy applications efficiently.          Ask ChatGPT

The AI Advantage: Building Career-Ready Skills in Classical and Generative AI

This fast-paced course guides learners through the transformative journey of artificial intelligence, from its classical foundations to state-of-the-art generative models. Each day focuses on a distinct theme—beginning with AI’s history and business impact, moving through data preparation and NLP fundamentals, and culminating with machine learning, deep learning, and generative AI. Students will explore IBM tools such as Watsonx and AutoAI, engage with real datasets, work with open-source AI pipelines from Hugging Face, and complete hands-on projects including insurance fraud detection and building retrieval-augmented solutions. By the end of the course, students will not only understand how modern AI works but also how to apply it meaningfully across various domains.
This intensive 2-day course provides a hands-on introduction to artificial intelligence, covering the evolution of AI, foundational machine learning and deep learning concepts, natural language processing, and the latest in generative AI. Through real-world tools like IBM watsonx and AutoAI; plus open-source generative pipelines from Hugging Face, students will apply what they learn to practical projects in fraud detection and retrieval-augmented generation.

AI impact in the world today

History and Evolution of AI

AI is good in finding the needle in the haystack

Machine learning methods

Resources

Assignment 1: Register with IBM Watsonx (pre-req before class starts)

The Evolution of AI

Natural Language Processing

Data Types

What’s so confusing about the confusion matrix

Assignment 2: Predict auto insurance fraud using IBM AutoAI

About Data

Machine learning explained

Regression and Classifiers Explained

Deep learning ecosystem

Machine Learning and Deep Learning

Overview of GenAI

Attention is all you need

It’s about cosine similarity

What’s next for GenAI

Is AI going to replace me?

Assignment 3: Build a Retrieval-Augmented Generation (RAG) solution using Watsonx GenAI services and benchmark it with other LLMs, such as ChatGPT, Mistral, Claude, and so forth.

Generative AI

Unlock your future with The AI Advantage! Gain career-ready skills in Classical & Generative AI through hands-on learning and real-world applications.

Google Sheets Mastery: Essential Skills for Modern Work and Business

This focused 3-hour course equips learners with practical and professional skills to work confidently in Google Sheets, one of the most powerful cloud-based spreadsheet tools. Participants will explore how to organize data, use essential functions, collaborate in real-time, and automate tasks—making them more productive in business, finance, project management, marketing, and more. Ideal for professionals and teams looking to get more out of Google Workspace.

null

Navigating the Google Sheets interface

Creating, saving, and sharing files in Google Drive

Keyboard shortcuts and quick tips

Module 1: Getting Started with Google Sheets

Entering and formatting data

Sorting, filtering, and using freeze panes

Key functions: SUM, AVERAGE, IF, VLOOKUP, COUNTIF

Data validation and drop-downs

Module 2: Working with Data and Functions

Creating charts (line, bar, pie)

Conditional formatting and color scales

Real-time collaboration and comments

Version history and protection features

Module 3: Visualization and Collaboration

Importing data from other Sheets or sources

Using Explore and built-in AI features

Intro to macros and basic automation

Templates for business use (budgets, schedules, trackers)

Module 4: Productivity Tools & Automation

Master Google Sheets for work & biz! Learn formulas, data analysis, automation & collaboration. Boost productivity with cloud-based spreadsheet skills.

Google Sheets Pro: Cloud-Based Spreadsheet Skills for Work and Business

Enroll Now

Enroll Now

Enroll Now

Book Your Live Virtual Class!

Live Virtual Classes

Free Virtual Classes

Test Course - Python for Beginners

Adobe PHOTOSHOP for Photo Editing, Image Retouching and Restoration

Discovering the Right Courses just for you

20+

2k+

Trending Categories

Generative AI – Masterclass

What Customers Say About Learneur

Discovering the Right Courses just for you