Generative AI Syllabus: A Step-by-Step Learning Path
Generative AI is changing the way industries work by allowing computers to create text, images, videos, and even music. It powers chatbots like ChatGPT, deepfake technology, and AI-generated art, with many more exciting applications.
If you want to learn this cutting-edge technology, you need a well-planned course that starts with the basics of AI and takes you to advanced deep learning models. In this blog, we’ll give you a clear and detailed breakdown of the Generative AI syllabus, based on the training program from Generative AI Masters.
Generative AI Syllabus
Module 1: Core Python Programming – The Foundation of AI and Machine Learning
Before diving into AI, deep learning, or Generative AI, it’s essential to build a solid foundation in Python.
This module is designed to help students develop fundamental programming skills needed to build AI applications, handle data, and use deep learning tools. Let’s explore the key topics covered in this module.
1. Python Basics: Understanding the Core Concepts
Python is a powerful, user-friendly, and beginner-friendly programming language, making it an ideal choice for those new to coding. Understanding the fundamentals of Python is essential for building a strong programming foundation.
Variables & Data Types – Discover how to store and manage different types of information, including text (strings), numbers (integers and floats), and true/false values (booleans).
Loops & Conditional Statements – Learn how to control the flow of a program by using loops (for, while) to repeat actions and conditional statements (if-else) to make decisions based on certain conditions.
Operators – Explore different types of operations, such as mathematical calculations (addition, subtraction), comparisons (greater than, less than), and logical expressions (and, or, not) that help in decision-making.
Indentation & Syntax – Unlike many other programming languages, Python organizes code using indentation instead of symbols like curly braces {}, making it more readable and structured.
Mastering these fundamental concepts will help students write clear, efficient, and error-free code—an essential skill for advancing in AI and Machine Learning.
Why is this important?
Learning Python basics is important because it helps you write clear, well-structured, and efficient programs—an essential skill for AI and ML development. Here are some everyday examples of how these concepts are useful:
- Chatbots & Virtual Assistants: AI assistants like Siri and Alexa use logic to decide how to respond. For example, if you ask about the weather, the AI checks the data and gives you the right answer using if-else conditions.
- Customer Data Analysis: Businesses analyze customer reviews and feedback to improve their services. Loops help process large amounts of data quickly, making it easier to find patterns.
- Self-Driving Cars: AI in autonomous vehicles constantly makes decisions, like stopping at a red light and moving when it turns green, using logical operations.
Social Media Recommendations: Platforms like YouTube and Instagram suggest videos and posts based on user preferences. AI organizes and analyzes data to personalize recommendations.
2. Data Structures: Organizing and Managing Data
Data structures help us store, organize, and manage data efficiently, making them essential for AI and Machine Learning. This module covers:
Lists – Ordered collections that can be changed (e.g., a list of student names: [“Alice”, “Bob”, “Charlie”]).
Dictionaries – Store data in key-value pairs, allowing quick lookups (e.g., storing a student’s details: {“name”: “Alice”, “age”: 25}).
Tuples – Immutable (unchangeable) sequences used to group related information (e.g., storing a point’s coordinates: (10, 20, 30)).
Sets – Unordered collections of unique items, useful for removing duplicates (e.g., a set of unique colors: {“red”, “blue”, “green”}).
Why is this important?
AI and ML applications work with huge amounts of data, so organizing it efficiently is crucial. Here are some simple examples:
Lists in AI: If you are building a chatbot, a list can store different responses, helping the AI pick the right one.
Dictionaries in ML: If an AI model is analyzing customer data, a dictionary can store user details like name, age, and purchase history for quick access.
Tuples in Image Processing: AI models use tuples to store image pixel values, ensuring data remains unchanged during processing.
Sets in Data Cleaning: When working with large datasets, sets help remove duplicate entries, making data more reliable for AI models.
By mastering data structures, students can efficiently handle information, making it easier to build smart and powerful AI applications!
3. Functions & Object-Oriented Programming (OOP)
Writing Reusable and Modular Code
When developing AI applications, writing clean and reusable code is crucial. This section focuses on:
Functions – Create reusable blocks of code to avoid repetition and make programs easier to manage.
Lambda Functions – One-line functions used for quick calculations or data processing. Object-Oriented Programming (OOP) – A powerful programming approach that organizes code using:
- Classes & Objects – Think of a class as a blueprint (like a recipe) and objects as the actual items created from it (like different cakes made from the same recipe).
- Encapsulation – Protecting data by restricting access to certain parts of a program.
- Inheritance – Allowing new code to reuse existing code without rewriting it.
Polymorphism – Making functions flexible so they can work with different types of data.
Why is this important?
AI models and machine learning frameworks like TensorFlow and PyTorch are built using OOP principles. Understanding these concepts helps AI developers write efficient and scalable programs.
Functions in AI: Imagine building a spam filter for emails. Instead of writing separate code for every email, a function can process all emails the same way, saving time.
Encapsulation in AI Security: In an AI-powered banking app, encapsulation helps protect sensitive customer information by restricting access to data.
Inheritance in AI Models: If a self-driving car AI needs to recognize traffic signs, it can reuse an existing image recognition model instead of starting from scratch.
Polymorphism in AI Assistants: Virtual assistants like Siri or Alexa use polymorphism to respond differently to various commands like “play music” or “set an alarm.”
By mastering these concepts, students will be able to write better AI programs, make code reusable, and work with advanced AI frameworks more effectively!
4. File Handling: Working with Data
Handling Files in AI and Machine Learning
AI and ML models rely on large datasets that are often stored in different file formats. This section covers essential file-handling techniques:
Reading & Writing Files – Learn how to open, read, and save data in formats like .txt, .csv, .json, and .xlsx.
Error Handling – Prevent program crashes by using try-except blocks to handle unexpected issues.
File Paths & Directories – Work with folders and files efficiently to organize and process data programmatically.
Why is this important?
AI projects require massive amounts of data, and this data is often stored in files. Whether you’re dealing with text, numbers, or images, knowing how to handle files is essential.
Reading Files in AI: Imagine building an AI-powered weather prediction system. You need to read historical weather data from a .csv file to train your model.
Error Handling in AI: If an AI program is analyzing thousands of financial records, a missing file or incorrect format could cause a crash. Using error handling ensures the program continues running smoothly.
Working with File Paths in AI: A face recognition AI might need to scan a folder containing thousands of images. Understanding how to navigate directories makes this process efficient and automated.
Mastering file handling helps students process real-world data effectively, making it a critical skill for AI and ML development!
5. Flask Framework: Introduction to Web Development
AI models are often used in real-world applications, and Flask—a lightweight web framework—makes it easy to deploy them as web apps. This section covers:
Building a Simple Web App – Create a basic AI-powered application using Flask.
Handling HTTP Requests – Learn how to take user inputs and return AI-generated responses.
Integrating AI Models – Connect AI models to web applications using APIs, making them accessible to users.
Why is this important?
Once you develop an AI model, you need a way for users to interact with it. Flask helps bring AI to life by allowing users to access AI-powered features through a simple web interface.
Example: AI Chatbots – If you create a chatbot using NLP, Flask lets users chat with it through a web page instead of running Python scripts manually.
Example: Recommendation Systems – Imagine an AI movie recommendation app. Flask can take a user’s favorite movie as input and return personalized suggestions.
Example: AI Image Classifier – If you build an AI model that identifies plant diseases from images, Flask can let farmers upload a picture and get instant diagnosis results.
By learning Flask, students can deploy their AI models as real-world applications and create interactive AI-powered websites!
6. Python Libraries: Essential Tools for AI & Data Science
Python offers powerful libraries that simplify AI and machine learning tasks. In this module, you’ll learn:
NumPy – Efficiently handle large datasets using multi-dimensional arrays and perform fast mathematical operations.
Pandas – Process and analyze data effortlessly, making it easier to clean and prepare datasets for AI models.
Matplotlib & Seaborn – Create visualizations like charts and graphs to better understand AI-generated data and patterns.
Why is this important?
AI and machine learning rely on huge amounts of data, and these libraries help process, analyze, and visualize that data efficiently.
Example: AI Data Processing (NumPy) – Imagine building a face recognition AI that processes thousands of images. NumPy helps store and manipulate image data quickly to speed up AI predictions.
Example: Data Analysis in AI (Pandas) – Suppose you’re analyzing a customer feedback dataset for a chatbot. Pandas helps clean and filter data, so your AI can understand user sentiments better.
Example: Visualizing AI Insights (Matplotlib & Seaborn) – If you’re working on a stock market prediction AI, visualizing price trends with graphs helps you and your AI understand patterns before making predictions.
Mastering these libraries allows students to efficiently work with AI data, making AI models more effective and accurate!
Why Is This Module Crucial for AI & Generative AI?
Python is the backbone of artificial intelligence, machine learning, and data science. This module provides students with the essential programming skills needed to succeed in AI.
Establish a Strong Foundation – Gain a solid grasp of programming fundamentals before tackling complex AI topics.
Learn to Process & Manage Data – AI applications rely on vast amounts of data. Knowing how to manipulate and analyze it is crucial.
Work with Real-World AI Datasets – Develop hands-on experience handling actual datasets and interpreting AI-generated insights.
Deploy AI Models with Flask – Learn how to make AI applications accessible through web-based platforms.
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Why Choose Generative AI Masters?
Industry-Relevant Curriculum – Learn from a syllabus curated by AI professionals, covering the latest advancements in AI and Generative AI.
Hands-On Learning & Real-World Projects – Gain practical experience by working on AI and ML projects that mirror industry challenges.
Mentorship from AI Experts – Get guidance from seasoned AI professionals with years of industry experience.
Career & Job Placement Support – Receive resume-building assistance, interview preparation, and job referrals to top AI companies.
Cutting-Edge AI Research & Innovation – Explore Large Language Models (LLMs), AI automation, and Generative AI breakthroughs.
Interactive Learning Experience – Participate in live training sessions, Q&A discussions, and AI development workshops for deeper understanding.
Globally Recognized Certification – Earn an AI certification that enhances your career prospects in AI, data science, and ML engineering.
Generative AI Syllabus
Module 2: Artificial Intelligence – What to Learn?
By the end of this module, students will understand the fundamentals of AI, how machines learn from data, and how AI models are built and improved.
What to Learn in Module 2: Artificial Intelligence
Introduction to AI & ML
- Understand the basics of Artificial Intelligence (AI) and its evolution.
- Learn the differences between AI, Machine Learning (ML), Deep Learning (DL), and Generative AI.
- Explore real-world applications of AI across industries.
Machine Learning Fundamentals
- Learn different types of ML: Supervised, Unsupervised, and Reinforcement Learning.
- Understand the ML workflow: Data collection, preprocessing, training, and evaluation.
- Explore essential ML algorithms like Regression, Classification, and Clustering.
- Identify the limitations and challenges of ML.
Deep Learning Concepts
- Learn how Neural Networks work and their key components (Perceptrons, Activation Functions).
- Understand backpropagation and optimization techniques for training deep models.
- Explore Convolutional Neural Networks (CNNs) for image processing.
- Study Recurrent Neural Networks (RNNs) for sequential data (speech, text).
- Learn about Transformer models and their role in NLP and AI advancements.
Generative AI & Advanced AI Models
- Discover how AI can generate text, images, videos, and music.
- Understand Generative AI techniques like GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
- Learn about GPT, BERT, and other transformer-based models used in AI chatbots and automation.
AI Tools & Hands-on Learning
- Get introduced to AI frameworks like TensorFlow, PyTorch, and Scikit-Learn.
- Learn how to implement ML and DL models using Python.
- Understand how to train, test, and deploy AI models in real-world applications.
Ethical AI & Future Trends
- Understand AI ethics, bias, and responsible AI development.
- Learn about the impact of AI on society and businesses.
- Explore the latest AI trends and career opportunities in AI and Generative AI.
Generative Ai Syllabus
Module 3: Python Programming
Essential Python for AI
- Master key Python concepts: Data types, control flow, functions, and OOP.
- Explore essential libraries: NumPy for numerical computing, Pandas for data manipulation, and Matplotlib for data visualization.
- Learn how to handle datasets, process information, and visualize trends.
Introduction to TensorFlow & Keras
- Understand the basics of TensorFlow, Google’s powerful AI framework.
- Learn how Tensors work and how to perform computations efficiently.
- Explore Keras, a high-level API for building and training AI models easily.
Building AI Models with TensorFlow & Keras
- Learn how to create neural networks using TensorFlow and Keras.
- Understand different layers (Dense, Convolutional, Recurrent) in deep learning models.
- Implement and train basic ML and DL models using Python.
Model Training & Evaluation
- Learn about loss functions, optimizers, and metrics to evaluate AI models.
- Understand overfitting and techniques to improve model performance (Regularization, Dropout, Batch Normalization).
Hands-on AI Development
- Work on real-world AI projects using TensorFlow and Keras.
- Train models on datasets and deploy them for AI applications.
- Learn how to fine-tune models for better accuracy and efficiency
Generative AI Syllabus
Module 4: Mathematics – Calculus & Vector Algebra
Foundations of Calculus for AI
- Understand functions, limits, and continuity – the core of AI learning.
- Understand slopes and rates of change (derivatives and gradients) essential for training machine learning models.
- Explore partial derivatives to analyze how AI models respond to different inputs
- Discover step-by-step learning (chain rule and backpropagation) in deep learning.
Mathematical Techniques for AI Model Training
- Learn about gradient descent, a key method for adjusting AI model parameters.
- Understand how AI reduces mistakes (loss functions) to make better predictions.
- Explore simple math tools (differentiation and integration) used in AI tasks like motion tracking.
Probability & AI Decision Making
- Learn about probability distributions and their role in AI predictions.
- Understand Bayes’ theorem and its use in AI models like spam filters.
- Explore random variables and how AI systems handle uncertainty.
Vector Math for AI Models
- Understand numbers, lists, and grids (scalars, vectors, and matrices) used in AI.
- Learn about simple math operations (dot and cross product) that AI uses.
- Discover how AI organizes and moves data (vector spaces and projections) for better accuracy.
Real-World Applications in AI
- Deep Learning: AI uses calculus to adjust itself for better learning.
- Computer Vision: AI processes images using math tools.
- Chatbots & Voice Assistants: AI understands language using probability and vectors.
Generative AI Syllabus
Module 5: Machine Learning:(Linear and Logistic Regression)
Predicting Numbers & Categories (Regression & Classification)
- Learn how AI predicts numbers (Regression) and categories (Classification).
- Understand how Linear Regression helps forecast trends (e.g., predicting house prices).
- Explore Logistic Regression, used for yes/no decisions (e.g., detecting spam emails).
- Learn about assumptions in regression models and when they work best.
- Master model evaluation techniques (accuracy, precision, recall, etc.) to check AI performance.
Data Segmentation with Clustering
- Discover how AI groups similar data using K-Means Clustering (e.g., customer segmentation).
- Learn about K-Means Clustering, a method used for grouping (e.g., organizing customers by shopping habits).
- Explore Hierarchical Clustering, which arranges data step by step to find patterns.
- Find out how businesses use clustering in marketing, healthcare, and image processing.
Bagging & Ensemble Learning
- Understand how combining multiple models improves accuracy (Ensemble Learning).
- Learn about Decision Trees, a method AI uses to make choices (e.g., approving bank loans).
- Explore Random Forest, a powerful AI model that uses many decision trees for better predictions.
- See how Bagging helps reduce mistakes and makes AI more reliable in areas like banking and fraud detection.
Generative AI Syllabus
Module 6: Machine Learning (Boosting)
- What is Boosting? – Learn how AI improves by fixing past mistakes and getting smarter over time.
- How Boosting Helps? – Understand how boosting makes AI models more accurate by focusing on difficult data points
- XGBoost (Extreme Gradient Boosting) – Discover one of the most powerful AI techniques used in competitions and real-world applications like fraud detection and risk assessment
- Why Boosting is Important? – See how boosting is used in banking, healthcare, and marketing to make better decisions.
Generative AI Syllabus
Module 7: Introduction to Neural Networks & Deep Learning
- How AI Learns Like the Human Brain – Understand how neural networks copy the way our brain processes information.
- Basics of Neural Networks – Learn about neurons, layers, and connections that help AI recognize patterns.
- How AI Improves Itself – Explore gradient descent, a method AI uses to reduce errors and improve accuracy.
- Real-World Applications – See how deep learning is used in face recognition, chatbots, self-driving cars, and more.
Generative AI Syllabus
Module 8 : Neural Network Architecture
- What is a Neural Network? – Learn how a neural network works like a human brain to process information
- Parts of a Neural Network – Understand the input, hidden, and output layers and their roles.
- How AI Makes Decisions – See how data moves through a network to recognize patterns and make predictions.
- Different Types of Neural Networks – Learn about simple and advanced networks used for tasks like image and speech recognition.
- Real-Life Uses – Discover how neural networks help in face detection, chatbots, and self-driving cars
Generative AI Syllabus
Module 9 : Backward Propagation
- How AI Processes Information – Understand how AI models take in data and learn from it.
- How AI Improves Itself – Learn how AI fixes its mistakes step by step to get better results.
- Why It’s Important – See how this process helps in voice assistants, facial, recognition, and recommendation systems.
- Simple Example – Just like learning to ride a bicycle, AI learns by making mistakes and adjusting to do better next time.
Generative AI Syllabus
Module 10: AI Model Settings & Fine-Tuning
Understanding Model Settings – Learn the difference between basic settings (parameters) and adjustable settings (hyperparameters) in AI models.
Training & Testing AI Models – Discover how AI is trained, tested, and improved using different data sets.
Fixing Learning Issues – Understand problems like slow learning, mistakes in predictions, and overfitting (when AI memorizes instead of learning).
Improving AI Performance – Learn simple techniques like dropout, tuning settings, and adjusting learning speed to make AI smarter.
Real-World Use – These techniques help in chatbots, medical diagnosis, and self-driving cars.
Generative AI Syllabus
Module 11: Optimizers – Improving AI Learning
- What Are Optimizers? – Learn how optimizers help AI models learn faster and make better predictions.
- Types of Optimizers:
1. Adam & AdaGrad – Help AI adjust learning speed based on data.
2. AdaBoost – Improves AI accuracy by learning from past mistakes.
3.RMSProp – Used for handling changing data patterns.
Why Optimizers Matter? – Without them, AI models may learn too slowly or struggle to improve.
Real-World Use – Optimizers are used in image recognition, speech-to-text, and self-driving technology.
Generative AI Syllabus
Module 12: Computer Vision – Bringing AI Vision to Life
Key Topics Covered:
Introduction to Computer Vision – How AI understands and processes images.
Working with Images in Python – Loading, displaying, and manipulating images.
Getting Started with OpenCV – A powerful library for image and video processing.
Essential Image Processing Techniques:
Converting Images – Transforming images into different formats (grayscale, binary, etc.).Resizing Images – Adjusting dimensions while preserving quality.
Applying Transformations – Rotating, flipping, and shifting images for better analysis.
Enhancing Contrast – Improving image visibility using contrast adjustments.
Generative AI Course Syllabus
Module 13: Understanding CNN Architecture – Powering AI Vision
Introduction to Convolutional Neural Networks (CNNs) – How AI processes and understands images.
Convolution & Feature Detection – Identifying important patterns in images.
Padding & Stride – Techniques to control image size during processing.
Activation Functions – Helping the network learn complex patterns.
Pooling Layers – Reducing image size while preserving key features.
Training a CNN – How CNNs learn from data to make accurate predictions.
Loss Function – Measuring and improving CNN performance.
Generative AI Course Syllabus
Module 14: Transfer Learning in Computer Vision – Leveraging Pre-Trained Models
- Object Detection Basics – How AI identifies objects in images.
- Bounding Box Concept – Marking objects within an image.
- Popular Object Detection Models – Understanding models like YOLO and Faster R-CNN.
- Pre-Trained Models – Using existing AI models for faster learning.
- Transfer Learning – Adapting powerful AI models to new tasks with minimal training.
- Image Segmentation – Breaking down images into meaningful parts.
Module 15: Advanced Convolutional Neural Networks (CNNs) – Enhancing Image Recognition
- Real-World Applications of Advanced CNNs – How AI processes images at an advanced level.
- Face Detection Techniques – Understanding how AI identifies faces in images and videos.
- RCNN & Variants – Exploring RCNN, Fast RCNN, Faster RCNN, and Mask RCNN for object detection.
- YOLO (You Only Look Once) – A high-speed object detection model used in real-time applications.
Module 16: Introduction to Natural Language Processing (NLP)
Text Pre-processing – Cleaning and preparing text data for AI models.
Binary Weight & Bag of Words (BoW) – Understanding how words are represented in AI models.
TF-IDF (Term Frequency-Inverse Document Frequency) – Identifying important words in a document.Spam Detection & Sentiment Analysis – Training AI to recognize spam messages and analyze emotions in text.
Named Entity Recognition (NER) – Teaching AI to identify names, places, and key information in text.
Module 17: Deep Learning for Natural Language Processing (DNLP)
- Word Vectors & Embeddings – Representing words as numerical data for AI models.
- Word2Vec – A popular technique for learning word relationships.
- CBOW (Continuous Bag of Words) & Skip-gram – Methods to predict words based on context.
Module 18: Introduction to Forecasting with Deep Learning
Understanding Recurrent Neural Networks (RNNs) – How they process sequential data.
Challenges in RNNs – Issues like vanishing and exploding gradients.
Real-World Applications – Time-series forecasting, speech recognition, and more.
Module 19: Advanced Forecasting with Deep Learning
- LSTM (Long Short-Term Memory) – How it improves long-term dependencies in data.
- GRU (Gated Recurrent Unit) – A simplified alternative to LSTM.
- RNN vs LSTM vs GRU – Key differences and when to use each model.
- Training RNN Models – Best practices for optimizing performance.
Generative AI Course Syllabus
Module 20: Sequence-to-Sequence (Seq2Seq) Models
Understanding Sequential Data – How AI processes time-dependent information.
Seq2Seq Model – A powerful technique for sequence prediction tasks.
Encoder-Decoder Architecture – How input sequences are transformed into meaningful outputs.
Real-World Applications – Machine translation, text summarization, and chatbot development.
Module 21: Advanced NLP Models
Introduction to Transformers – How modern NLP models process language.
Transformer Architecture – Understanding key components and how they improve AI performance.
Pre-Trained Models – Overview of powerful models like BERT, T5, and ELMo.
Attention Mechanism – Exploring Self-Attention, Multi-Head Attention, and Transformer Blocks.
Generative AI Course Syllabus
Module 22: Generative AI
Understanding Generative AI – How AI creates text, images, and more
Large Language Models (LLMs) – Exploring how AI understands and generates human-like text.
Introduction to GPT – Learn how models like ChatGPT generate conversations.
Variational Autoencoders (VAE) & GANs – How AI generates realistic images and media.
Module 23: GANs and Large Language Models (LLMs)
- Introduction to GANs (Generative Adversarial Networks) – Understanding how AI generates realistic images and media.
- DCGAN (Deep Convolutional GANs) – Learning how to generate images using the MNIST dataset.
- Building the Generator & Discriminator – Exploring how GANs create and refine outputs.
- Loss Calculation – Understanding how AI models improve their outputs over time.
- WGAN (Wasserstein GAN) – Learning advanced GAN techniques for better stability and quality.
Generative AI Course Syllabus
Module 24: Transfer Learning in NLP
- Pretrained NLP Models – Explore powerful AI models like GPT, BERT, BART, and T5.
- Applications of Pretrained Models – Learn how these models improve tasks like text generation, translation, and sentiment analysis.
- Introduction to Transformer Encoder Models – Understand different transformer-based models such as:
1. BERT (Bidirectional Encoder Representations from Transformers) – Improves text understanding.
2. RoBERTa – A more advanced and efficient version of BERT.
3. DistilBERT – A lightweight, faster version of BERT for practical applications.
Module 25: Autoencoders
- Introduction to Autoencoders – Understand how these models compress and reconstruct data.
- Types of Autoencoders – Explore different types, including Sparse, Denoising, and Variational Autoencoders (VAE).
- Real-World Applications – Learn how autoencoders are used in:
1. Image noise reduction
2. Anomaly detection
3. Data compression
4. Feature extraction
Generative AI Course Syllabus
Module 26: AI Applications
- Introduction to AI-Powered Applications – Understand how AI is transforming everyday technology.
- Exploring ChatGPT, Bing AI, and Bard – Learn how these models work and their key differences.
- Real-World Use Cases – Discover applications in:
1. Conversational AI (Chatbots & Virtual Assistants)
2. Content Generation & Summarization
3. Search Engine Enhancements
4. Code Generation & Debugging
Module 27: Speech Analytics
- Introduction to Speech Analytics – Understanding how AI processes and analyzes speech.
- Automated Speech Recognition (ASR) – Converting spoken language into text for various applications.
- Text-to-Speech (TTS) Conversion – How AI generates human-like speech from text.
- Voice Assistant Technologies – Exploring AI-powered voice assistants like Alexa, Siri, and Google Assistant.
Generative AI Course Syllabus
Module 28: Reinforcement Learning - Part 1
- Introduction to Reinforcement Learning (RL) – Understanding how AI learns through rewards and actions.
- Q-Learning Concept with Examples – Learning how AI makes decisions using Q-values.
- Real-World Applications of Q-Learning – Examples from gaming, robotics, and finance.
- Exploration vs. Exploitation – Balancing new discoveries with known strategies to improve AI learning.
Module 29 : Reinforcement Learning-2
- Policy Gradient Concepts – How AI improves decision-making using policy-based learning.
- Actor-Critic Methods – Understanding how AI balances learning strategies for better performance.
- Proximal Policy Optimization (PPO) – A widely used RL algorithm for stable learning.
- Working with Deep RL Libraries – Hands-on experience with tools for training AI agents.
Generative AI Course Syllabus
Module 30: Generative AI & Large Language Models (LLMs)
- Understanding LLMs & Real-World Applications
How LLMs work and their impact on AI.
Case studies of AI chatbots and content generation.
Hands-on chatbot creation using LLMs. - Fine-Tuning LLMs for Specific Tasks
Understanding LSTM vs GRU for text generation.
Fine-tuning transformer-based models like GPT & LLAMA-2.
Step-by-step fine-tuning process for improving AI performance. - Evolution of Generative AI
History and advancements in Generative AI.
Understanding GPT models and prompt engineering techniques.
Managing AI challenges like hallucinations in LLMs. - Practical Implementation: Finetuning LLAMA-2
Basics of fine-tuning an LLM.
Simple finetuning techniques for LLAMA-2.