Generative AI with OpenAI, Google Gemini, Hugging Face: Master Cutting-Edge AI
Master the cutting-edge field of Generative AI with BinnBash Academy's comprehensive course. Dive deep into Large Language Models (LLMs), Diffusion Models, and advanced architectures. Learn to leverage OpenAI, Google Gemini, and Hugging Face for text, image, and multimodal generation. Build a powerful portfolio with intensive real-time live projects to become a cutting-edge Generative AI Engineer or AI Research Scientist!
Create the Future of AI!Who Should Enroll in this Generative AI Course?
This course is ideal for individuals eager to dive deep into creating intelligent content and systems:
- Aspiring Generative AI Engineers & AI Research Scientists.
- Machine Learning Engineers looking to specialize in generative models.
- Data Scientists seeking to apply generative AI to real-world problems.
- Software Developers with Python experience interested in building AI-powered creative applications.
- AI Researchers and enthusiasts eager to explore the frontier of AI content generation.
Generative AI Course Prerequisites
- Solid understanding of Python programming (intermediate to advanced).
- Foundational knowledge of Machine Learning and Deep Learning concepts.
- Basic understanding of neural networks (e.g., CNNs, RNNs, basic Transformers).
- Familiarity with TensorFlow/Keras or PyTorch is highly recommended.
- A strong analytical and creative problem-solving mindset.
Key Generative AI Tools & Concepts Covered
Hands-on mastery of leading Generative AI platforms, advanced model architectures, and practical techniques for creating intelligent content and systems.
Generative AI: Comprehensive Syllabus & Intensive Real-Time Projects
Module 1: Introduction to Generative AI & Core Concepts
- What is Generative AI? Differentiating from Discriminative AI.
- Applications of Generative AI: Text, Image, Audio, Video.
- Overview of Key Architectures: GANs, VAEs, Flow-based Models, Diffusion Models.
- Understanding Latent Space and Sampling.
- Ethical Considerations and Responsible AI in Generative Models.
- Live Project: Implement a basic VAE (Variational Autoencoder) on a simple dataset (e.g., MNIST) to understand latent space representation and image generation.
Tools & Concepts:
- Python, TensorFlow/PyTorch, NumPy.
Expected Outcomes:
- Grasp core Generative AI concepts.
- Understand different generative model types.
- Implement a basic generative model.
Module 2: Large Language Models (LLMs) & Transformers
- Deep Dive into Transformer Architecture: Encoder, Decoder, Self-Attention.
- Evolution of LLMs: GPT-series, BERT, T5 (architectural overview).
- Tokenization and Embeddings for LLMs.
- Generative Strategies: Beam Search, Top-K, Nucleus Sampling.
- Introduction to Instruction Tuning & Reinforcement Learning from Human Feedback (RLHF) - concepts.
- Live Project: Use a pre-trained Transformer model (e.g., GPT-2) from Hugging Face for various text generation tasks (e.g., story writing, code snippets, dialogue).
Tools & Concepts:
- Hugging Face Transformers, Python.
Expected Outcomes:
- Understand Transformer architecture.
- Work with pre-trained LLMs.
- Generate coherent and creative text.
Module 3: OpenAI API & Applications
- Introduction to OpenAI Platform and APIs.
- Text Generation with GPT-3.5 / GPT-4 via API: Prompting best practices.
- Image Generation with DALL-E API: Text-to-Image, Image Editing.
- Speech-to-Text with Whisper API.
- Embedding Models for Semantic Search and RAG.
- Live Project: Build a web application using Flask/Streamlit that integrates OpenAI's GPT for text generation and DALL-E for image generation based on user prompts.
Tools & Concepts:
- OpenAI API, Python `requests`, Flask/Streamlit.
Expected Outcomes:
- Integrate OpenAI APIs into applications.
- Master prompting for text and image generation.
- Build practical OpenAI-powered apps.
Module 4: Google Gemini API & Multimodal Generative AI
- Introduction to Google Gemini Model and its multimodal capabilities.
- Interacting with Gemini API for text, image, and combined inputs.
- Multimodal Prompting: Crafting prompts for diverse input types.
- Advanced features of Gemini: Function Calling, Safety Settings.
- Comparison of Gemini with other LLMs (strengths and use cases).
- Live Project: Develop a multimodal application using Google Gemini API that can understand images and text simultaneously to answer complex queries or generate descriptive content.
Tools & Concepts:
- Google Gemini API, Python, multimodal data handling.
Expected Outcomes:
- Leverage Google Gemini for multimodal tasks.
- Understand multimodal prompting.
- Build cutting-edge Gemini applications.
Module 5: Hugging Face Ecosystem: Fine-tuning & Diffusion Models
- Hugging Face Ecosystem: Transformers, Datasets, Accelerate, PEFT.
- Parameter-Efficient Fine-Tuning (PEFT): LoRA, QLoRA for LLMs.
- Deep Dive into Diffusion Models: Denoising Diffusion Probabilistic Models (DDPMs).
- Using Hugging Face Diffusers Library for Image Generation.
- Customizing Diffusion Models: Textual Inversion, DreamBooth (concepts).
- Live Project: Fine-tune a smaller open-source LLM using LoRA on a custom dataset for a specific domain, and generate images using a pre-trained Diffusion model from Hugging Face.
Tools & Concepts:
- Hugging Face (Transformers, Diffusers, PEFT), PyTorch/TensorFlow.
Expected Outcomes:
- Fine-tune LLMs efficiently.
- Generate images with Diffusion Models.
- Customize generative models.
Module 6: Generative AI Deployment, MLOps & Intensive Capstone Projects
- Deployment Strategies for Generative AI Models: API-based, serverless, cloud services.
- Containerization with Docker for generative AI applications.
- MLOps for Generative AI: Model versioning, monitoring (bias, safety, performance), CI/CD.
- Evaluating Generative Models: Perplexity, FID, CLIP Score, Human Evaluation.
- Scaling Generative AI: Inference optimization, cost management.
- Intensive Real-time Capstone Project: Develop and deploy an end-to-end Generative AI solution for a real client or a complex simulated problem. This could be a sophisticated content creation platform, an intelligent design assistant, or a personalized multimodal chatbot, integrating models from OpenAI, Google Gemini, and/or Hugging Face, building a user interface, and deploying it to a cloud environment.
- Building a Professional Generative AI Portfolio: Showcasing deployed applications, creative outputs, and MLOps practices.
- Career Guidance: Generative AI Engineer, AI Research Scientist (Generative AI), Prompt Engineer, Creative AI Developer, MLOps Engineer (Generative AI), AI Solutions Architect, Mock Interviews.
Tools & Concepts:
- Flask/FastAPI, Docker (concepts), Cloud platforms (concepts), MLOps tools.
- Intensive Live Project Work, Client Communication, Portfolio Building, Career Prep.
Expected Outcomes:
- Deploy Generative AI models into production.
- Implement MLOps for generative AI.
- Gain extensive practical experience with real-world Generative AI project lifecycle, leading to tangible, deployable creative AI solutions.
- Prepare for a high-level Generative AI career.
This course provides hands-on, in-depth expertise to make you a proficient and job-ready Generative AI professional, with a strong emphasis on practical application, real-time project implementation, and building a powerful, results-driven portfolio!
Generative AI Professional Roles and Responsibilities in Real-Time Scenarios & Live Projects
Gain hands-on experience by working on live projects, understanding the real-time responsibilities of a Generative AI professional in leading tech companies, AI research labs, and innovative startups. Our curriculum aligns with industry demands for cutting-edge Generative AI practitioners.
Generative AI Engineer
Develops and deploys models for generating text, images, and other content, as done at OpenAI.
AI Research Scientist (Generative AI)
Conducts research into new generative models, architectures, and training techniques, similar to work at Google DeepMind.
Prompt Engineer
Specializes in crafting and optimizing prompts to guide generative models for desired outputs, common at Anthropic.
Creative AI Developer
Builds applications that leverage generative AI for artistic creation, design, and content generation.
MLOps Engineer (Generative AI)
Manages the lifecycle of generative models, including deployment, monitoring, and continuous improvement.
Applied AI Scientist (Generative AI)
Applies generative AI techniques to solve specific business problems and create innovative product features.
AI Solutions Architect (Generative AI)
Designs comprehensive system architectures that incorporate generative AI components.
NLP Engineer (Generative Focus)
Focuses on text generation, summarization, and conversational AI using LLMs and other generative NLP models.
Our Alumni Works Here!
Arjun Sharma
Generative AI Engineer
Nisha Patel
AI Research Scientist
Rahul Verma
Prompt Engineer
Divya Singh
Creative AI Developer
Siddharth Rao
MLOps Engineer (GenAI)
Kavya Gupta
Applied AI Scientist
Akash Kumar
AI Solutions Architect
Priya Reddy
NLP Engineer
Rohan Joshi
Generative AI Trainee
Ananya Sharma
Senior GenAI Engineer
Arjun Sharma
Generative AI Engineer
Nisha Patel
AI Research Scientist
Rahul Verma
Prompt Engineer
Divya Singh
Creative AI Developer
Siddharth Rao
MLOps Engineer (GenAI)
Kavya Gupta
Applied AI Scientist
Akash Kumar
AI Solutions Architect
Priya Reddy
NLP Engineer
Rohan Joshi
Generative AI Trainee
Ananya Sharma
Senior GenAI Engineer
What Our Generative AI Students Say
"This Generative AI course is mind-blowing! I can now create incredible text and images with OpenAI and Gemini. Truly cutting-edge!"
"The deep dive into Diffusion Models and fine-tuning with Hugging Face was invaluable. I feel equipped to tackle any generative task."
"As an AI enthusiast, understanding multimodal generative AI with Google Gemini was a game-changer. The practical projects were amazing."
"BinnBash Academy's focus on MLOps for Generative AI and deploying models to the cloud truly sets it apart. I gained production-ready skills."
"The instructors are highly knowledgeable and provide cutting-edge insights into ethical AI and responsible generative model development."
"I highly recommend this course for anyone looking to be at the forefront of AI. It's comprehensive, challenging, and prepares you for the future of creative AI."
"From mastering OpenAI APIs to customizing models with LoRA, every aspect was covered in detail. I can now build truly innovative AI applications."
"The emphasis on building a professional portfolio with deployed generative AI applications and career guidance was extremely helpful. BinnBash truly supports your job search."
"Learning about prompt engineering for both text and image generation gave me powerful control over AI outputs."
"The practical approach to learning, combined with deep theoretical understanding and intensive real-time projects, made this course stand out from others."
Generative AI Job Roles After This Course
Generative AI Engineer
AI Research Scientist (Generative AI)
Prompt Engineer
Creative AI Developer
MLOps Engineer (Generative AI)
Applied AI Scientist (Generative AI)
AI Solutions Architect (Generative AI)
NLP Engineer (Generative Focus)