Generative AI Online Training
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Generative AI
Our Introduction to Generative AI course provides learners with a comprehensive foundation in generative models, covering essential concepts and advanced applications. Beginning with the fundamentals, participants will understand the core principles of Generative AI, distinguish between generative and discriminative models, and explore popular tools in the field. The course also delves into ethical considerations and practical skills such as prompt engineering and advanced techniques like autoencoders and GANs. Learners will tackle advanced topics, including large language models (LLMs), fine-tuning them for specific purposes, and applying reinforcement learning. The program emphasizes real-world applications across industries like software development and marketing, offering hands-on experience in generating code, images, and chatbots using frameworks such as Rasa and ChatGPT API
Why should you choose Generative AI Course?
Choosing a Generative AI course equips you with cutting-edge skills in one of the most transformative areas of technology, enabling you to harness AI for creative, technical, and practical applications across industries. It prepares you for in-demand roles, such as AI Engineer or Data Scientist, by teaching you to build, deploy, and optimize AI models for tasks like text generation, image creation, and personalized solutions. With hands-on experience in tools like TensorFlow, PyTorch, or OpenAI APIs, and insights into ethical considerations, a Generative AI course not only enhances your career prospects but also empowers you to innovate, solve complex problems, and stay ahead in a rapidly evolving field.
Objectives of Generative AI Online Training
The objectives of Generative AI Training are designed to equip learners with the knowledge, skills, and practical experience to leverage generative AI technologies effectively. Below are the key objectives
- Foundational Knowledge
- Practical Skills
- Application Development
- Problem-Solving
- Ethical and Responsible AI
- Industry-Relevant Expertise
- Innovation and Creativity
- Collaboration and Interdisciplinary Integration
Who Can Learn
- Data Scientists and AI Engineers: To deepen expertise in advanced AI and machine learning techniques.
- Software Developers: To expand skillsets by learning how to integrate generative models into applications.
- Creative Professionals: Such as designers, artists, and content creators, interested in leveraging AI for art, animation, or personalized content.
- Marketers and Advertisers: To automate and enhance personalized campaigns using generative content.
- Computer Science and Engineering Students: To build a strong foundation in machine learning and AI for future careers.
- Researchers: In fields like healthcare, psychology, or social sciences, looking to use generative AI for simulations or data augmentation.
Prerequisites to Learn Generative AI Course
- Basic Programming Skills: Familiarity with Python is highly recommended.
- Understanding of AI/ML Basics: Concepts like machine learning, neural networks, and supervised learning.
- Mathematics: Knowledge of linear algebra, calculus, probability, and statistics.
- Familiarity with AI Tools: Experience with libraries like TensorFlow or PyTorch is helpful but not mandatory.
- Curiosity and Creativity: A willingness to experiment and solve problems using AI techniques
- You need background skills like basic programming knowledge, mathematics, statistics, and machine learning fundamentals to learn both skills.
Generative AI Course Curriculum
Duration: 60 Hours
- Basics of Python Programming
- Installation and Setup:
- Installing Python and setting up a development environment (IDEs
like PyCharm, VSCode, Jupyter Notebooks)
- Installing Python and setting up a development environment (IDEs
- Syntax and Basic Constructs:
- Variables and data types (integers, floats, strings, booleans)
- Basic input and output
- Comments and documentation
- Control Structures
- Conditional Statements:
- if, elif, else
- Loops:
- for, while
- Loop control statements (break, continue, pass)
- Functions
- Defining Functions:
- Parameters and return values
- Scope and Lifetime:
- Local and global variables
- Lambda Functions:
- Anonymous functions
- Introduction to Generative AI
- AI vs ML vs DL vs NLP vs Generative AI
- Generative AI principles
- What is the role of ML in Gen-AI
- Different ML techniques (Supervised, Unsupervised, Semisupervised & Reinforcement Learning)
- Applications in various domains
- Ethical considerations
- NLP essentials
- Basic NLP tasks
- Different text classification approaches
- Frequency based – Bag of words,TF-IDF, N-gram.
- Distribution Models – CBOW, Skipgram(Traditional approaches)and
word2vec, Glove. - Ensemble Methods (Random Forest, Gradient Boosting, AdaBoost) &
Traditional Machine Learning Models – Naïve Bayes, Support Vector
Machine (SVM), Decision Trees, Logistic Regression. - Deep learning techniques – CNNs, RNNs, LSTMs, GRU and
Transformers.
- Autoencodes
- VAE’s and applications
- GAN’s and it’s applications
- Different types of GAN’s and applications
- Different types of Language models
- Applications of Language models
- Transformers and its architecture
- BERT, RoBERTa, GPT variations
- Applications of transformer models
- What is Prompt Engineering
- What are the different principles of Prompt Engineering
- Types of Different Prompt Engineering Techniques
- How to Craft effective prompts to the LLMs
- Priming Prompt
- Prompt Decomposition
- Generative AI lifecycle
- What is RLHF
- LLM pre-training and scaling
- Different Fine-Tuning techniques
- What are word embeddings
- What is the use of word embeddings, where we can use it?
- Word Embeddings – Word2Vec, GloVe and FastText
- Contextual Embeddings – ELMo , BERT and GPT
- Sentence Embeddings – Doc2Vec, Infersent, Universal Sentence
Encoder - Subword Embeddings – BPE(Byte Pair Encoding), Sentence Piece
- Usecase of Embeddings.
- What is Chunking
- What is the use of chunking the document
- What are the traditional effective chunking techniques
- What are the problems and limitations with traditional chunking
techniques? - How to overcome the limitations of Traditional chunking
- Advanced Chunking Techniques:
- Character Splitting
- Recursive Character Splitting
- Document based Chunking
- Semantic Chunking
- Agentic Chunking
- What is RAG
- What are the main components of RAG
- High level architecture of RAG
- How to Build RAG using external data sources
- Advanced RAG
- What is Langchain
- What are the core concepts of Langchain
- Components of Langchain
- How to use Langchain agents
- LlamaIndex
- What are Vector Databases
- Why do we prefer Vector Databases over Traditional Databases
- Different Types of Vector Databases: OpenSource and Close Source
- OpenSource: Chroma DB, Weaviate,Faiss,Qdrant
- Close-Source Vector Databases:Pinecone,ArangoDB,Cloud-Based
Solutions
- Supervised Finetuning
- Repurposing-Feature Extraction
- Advanced techniques in Supervised Finetuning -PEFT -LoRA, QLoRA
- Text based LLMs
- Automatic Evaluation: BULE Score, ROUGE Score, METEOR, BERT
Score. - Human Evaluation: Coherence, Factuality, Originality, Engagement
- Automatic Evaluation: BULE Score, ROUGE Score, METEOR, BERT
- Image based LLMs
- Automatic Evaluation: Pixel-level metrics, FID (Frechet Inception
Distance), IS (Inception Score), Perceptual Quality Metrics,
Diversity Metrics. - Human Evaluation: Photorealism, Style, Creativity, Cohesiveness
- Automatic Evaluation: Pixel-level metrics, FID (Frechet Inception
- Audio generation LLMs
- Automatic Evaluation:
- FAD (Frechet Audio Distance),
- IS (Inception Score),
- Perceptual Quality Metrics – PAQM,
- PAQM – SNR (Signal-to-Noise Ratio),
- PAQM – PESQ (Perceptual Evaluation of Speech Quality)
- Human Evaluation:
- Perceptual Quality – PQ,
- PQ- Naturalness,
- PQ-Fidelity,
- PQ- Musicality,
- Task Specific Evaluation.
- Automatic Evaluation:
- Video Generation LLMs
- Automatic Evaluation: FVD (Frechet Video Distance), Inception
Score(IS), Perceptual Quality Metrics, Motion Based Metrics –
Optical Flow Error, Content-Specific Metrics. - Human Evaluation: Visual Quality, Temporal Coherence, Content
Fidelit.
- Automatic Evaluation: FVD (Frechet Video Distance), Inception
- Model Deployment and Management
- Scalability and Performance Optimization
- Security and Privacy
- Monitoring and Logging
- Cost Optimization
- Model Interpretability and Explainability.
- Amazon Bedrock, Azure OpenAI
- ChatGPT, Gemini, Copilot
Trainer Information
Our Generative AI trainer is a seasoned professional with a strong blend of real-world experience and exceptional training expertise. With years of hands-on work in developing AI-powered solutions for diverse industries, they bring practical insights and up-to-date knowledge to every session.
- Industry Expertise: Extensive real-time experience in Data Science and AI across diverse sectors.
- Seasoned Trainer: Over [X years] of delivering impactful training to professionals and students.
- Comprehensive Modules: Proficient in teaching Machine Learning, Deep Learning, NLP, and Generative AI.
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