Summary
Highlights
This section introduces Generative AI as a groundbreaking technology capable of creating new content like text, images, music, and videos. It highlights the importance of mastering GenAI for staying ahead in the tech world and outlines the course's scope, including machine learning, deep learning, LLM prompting, and multimodal AI. It also mentions a professional certificate course in collaboration with ENIT Academy.
This part explains what Generative AI is, how it works, and its everyday use cases. It covers how AI can simplify daily tasks through automation, enhance productivity, and provide personalized assistance. Key technologies like Large Language Models (LLMs) and Natural Language Processing (NLP) are introduced as the foundation of GenAI.
This segment focuses on the responsible and ethical use of AI, emphasizing fairness, transparency, and accountability. It discusses understanding AI limitations, verifying AI-generated information, and addressing potential biases (gender, racial, cultural, confirmation, socioeconomic). The section also provides examples of effective prompts using Microsoft Copilot.
This delves into the mechanics of LLMs, explaining their evolution from RNNs to transformer models. It details how transformers tokenize, encode, and embed text, using an attention mechanism for context understanding. Examples of powerful LLMs like GPT-3 and Megatron Turing NLG 530B are discussed, along with their capabilities in generation, summarization, translation, classification, and chatbot functionality across various industries.
This chapter simplifies Generative AI for beginners, explaining it as AI that creates new content. It uses examples like ChatGPT for text generation, DALL-E for image generation, and GPT-3 for text and logic tasks. A comparison between traditional AI (analysis and decision-making) and Generative AI (content creation) is provided, highlighting the transformative impact of GenAI across text, images, music, and coding.
This section explains the three major families of generative AI models: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models. It details how each works, their applications (e.g., realistic faces with GANs, anomaly detection with VAEs, chatbots with Transformers), and their respective strengths and challenges. Guidance is provided on when to use each model based on the task type.
A practical demonstration of building a generative AI model using a pre-trained model (GPT-2) from Hugging Face is provided. The steps include setting up the environment in Google Colab, installing necessary libraries, loading the model and tokenizer, and generating text based on a given prompt. The concepts of max length, temperature, and num return sequence parameters are explained.
This part explores how Generative AI is transforming various industries. Examples include: healthcare (faster diagnosis, drug discovery), finance (smarter risk management, report generation), retail (personalized shopping, virtual try-ons), manufacturing (faster design, predictive maintenance), education (AI tutors, automated feedback), and entertainment (scriptwriting, virtual characters).
This section guides through building a chatbot in Google Colab using the Hugging Face Transformers library and the DialoGPT Small model. It covers importing libraries, loading the model and tokenizer, initializing chat history, encoding user input, managing conversation context, and generating and decoding responses. The importance of chatbots for user engagement and automation is also discussed.
This concluding segment addresses the limitations of generative AI models, such as lack of true understanding, dependence on data quality, creativity limitations, ethical concerns, context and nuance issues, high cost, and data privacy. It then discusses the future of GenAI, including improved understanding, enhanced creativity, more ethical AI, better personalization, energy efficiency, human-AI collaboration, and integration with new technologies.
This section introduces generative AI agents as advanced AI-driven systems capable of autonomous task performance, content generation, and decision-making using LLMs. It covers the evolution of AI agents from rule-based to machine learning-based and then to generative AI agents. Key benefits like efficiency, scalability, and intelligence are discussed, along with a focus on OpenAI’s Operator and Deep Research agents.
This segment breaks down the core components of generative AI agents, including foundation models (GPT-4, DALL-E, Whisper), prompt engineering, context handling, memory storage, tool integration, and autonomous decision-making. It then explains how these agents work through input processing, context awareness, intelligent response generation (RAG, Chain of Thought reasoning), and fine-tuning.
This part covers the architecture and deployment of AI agents, distinguishing between standalone and multi-agent systems, and the role of agent orchestrators. It discusses cloud-based versus edge-based agents and the use of APIs for building them. The section also details how to enhance AI agents by integrating with databases, web scrapers, APIs, and vector databases for context recall, and introduces autonomous agents like AutoGPT.
This section addresses the challenges and limitations of generative AI agents, including ethical considerations (bias, hallucination), computational cost, security risks (data leaks, jailbreaking), and legal/compliance issues. It then explores future trends and advancements, such as AI agents with emotional intelligence, self-improving AI systems, the potential evolution into AGI, and the role of AI agents in the future of work.
This hands-on tutorial introduces Google's ADK, an open-source project designed to simplify building complex, production-ready AI agents, especially multi-modal ones. It highlights ADK's model-agnostic, deployment-agnostic, and interoperable nature. The tutorial walks through setting up the environment, installing ADK, and demonstrates how to clone and run sample agents from Google's GitHub, including YouTube shorts generation.
This part explains the Agent-to-Agent (A2A) protocol introduced by Google, emphasizing its role in enabling AI agents to communicate, collaborate, and delegate tasks across heterogeneous systems. It uses an HR example to illustrate the need for standardized communication among agents. The video also details the Multi-Agent Collaboration Protocol (MCP) as an open-source standard for supplying context to LLMs, similar to USB-C for hardware connections.
This segment introduces LLM chatbots as intelligent virtual agents powered by LLMs like GPT and Gemini. It covers setting up the development environment, data recollection and preparation, implementing LLMs, and using techniques like chunking and embedding for vector search (RAG). The tutorial also touches on query processing, zero-shot text classification, and sentiment analysis with LLMs.
A practical demonstration of developing an LLM chatbot using LangChain and OpenAI APIs. It covers installing necessary libraries (LangChain, Streamlit), securely setting up OpenAI API keys, initializing the LLM model (e.g., GPT-4), and setting up memory for conversational context. The tutorial then guides through designing a simple web-based user interface with Streamlit to interact with the chatbot and view chat history.
This segment explains deep learning as a subset of machine learning that uses neural networks inspired by the human brain. It details how deep neural networks process data through multiple layers, automatically discovering representations from raw data. The three types of deep learning applications (supervised, unsupervised, reinforcement learning) and common algorithms like CNNs and RNNs are discussed. Real-world applications in autonomous vehicles, healthcare, NLP, and predictive maintenance are highlighted, along with advantages and disadvantages.
This course guides users from foundational concepts of LLMs and Gemini's unique capabilities to advanced prompting techniques and real-world applications. It starts with an introduction to Gemini, covering its core architecture, key features, and variations (Gemini 2.5 Pro, Flash, Flash Light). It also explains how Gemini generates photos and videos using Imagine 2 technology.
This module explores advanced prompting techniques to unlock Gemini's full potential for complex tasks. It covers Chain of Thought prompting, encouraging reasoning for logical problem-solving, and self-consistency for improved answer reliability. Knowledge generation prompting is introduced for synthesizing structured content, and prompt templates are discussed for ensuring consistent and high-quality outputs, especially for scalable solutions. A case study on improving chatbot responses is also presented.
This section focuses on two common applications of Gemini: text generation and summarization. It explains how to prompt Gemini for different writing styles and tones, from poems to blog posts. Techniques for effective summarization, including extractive and abstractive methods, are detailed. The concept of explicitly instructing Gemini to adopt a specific persona or style is covered, along with streamlining content creation from ideation to repurposing. A practical exercise involves generating a 500-word blog post on remote work benefits.
This module explores Gemini's analytical and logical capabilities for building robust question-answering systems and generating code. It discusses how to make Gemini answer questions based on specific documents using Retrieval Augmented Generation (RAG) and how to frame prompts for different question types (factual, explanatory, comparative, contextual). The section also covers Gemini’s ability to generate and debug code in various programming languages, accelerating the development process. A case study on a VS code extension as a code assistant is presented.
This crucial module shifts focus from creating to refining prompts, emphasizing that prompt engineering is a continuous cycle. It covers defining clear success metrics (accuracy, fluency, relevance), A/B testing different prompt versions, and analyzing failures (hallucinations, following problems, verbosity issues, tone mismatch) to identify areas for continuous improvement. The importance of prompt engineering platforms for managing, testing, and deploying prompts systematically is also highlighted.
This final module covers the practical steps of bringing Gemini-powered applications to life, from prototype to product. It details using the Gemini API for integration, including authentication and structured requests. Fine-tuning is discussed as a way to further train Gemini on specific data for specialized use cases. Deployment strategies using cloud platforms and serverless functions are explored, along with continuous monitoring of API latency, error rates, and response quality. Crucially, prompt security (preventing injection) and ethical AI development principles (fairness, transparency, accountability) are emphasized.
This section introduces multimodal prompting as a technique allowing AI models to interpret and respond to input from multiple modalities (text, images, audio, video) simultaneously. It explains the evolution of prompting from text-only to multimodal systems and how models understand and process various input types through specialized architectures and shared embedding spaces. Key differences between unimodal and multimodal prompting, along with challenges like input misalignment and computational load, are also covered.
This part discusses tools and platforms supporting multimodal prompting, including ChatGPT with vision, Google Gemini, Anthropic's Claude, and OpenAI's DALL-E. It then explores various use cases and applications such as visual Q&A, content generation, product tagging, interactive education, and accessibility. A practical demo using ChatGPT showcases its multimodal capabilities for tasks like summarizing movie plots, generating images from text, analyzing stock charts, and solving mathematical problems from images, demonstrating text-to-image and image-to-text reasoning.
This segment introduces deep learning as a subset of machine learning, explaining its ability to automatically discover representations from raw data using neural networks. It describes the structure of artificial neural networks, including input, hidden, and output layers, and how they learn through weighted sums and backpropagation. Different types of neural networks are discussed: Feedforward Neural Networks (FFN), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), highlighting their applications in image, speech, and natural language processing.
This tutorial focuses on automating Google Sheets using N8N, a powerful workflow automation tool. It defines N8N as an open-source tool for connecting various applications and databases without extensive coding, offering a visual editor, over 350 integrations, and self-hosting options. The section explains how N8N works through nodes (tiny workers) and workflows (sequence of nodes), triggers (event-driven, scheduled), and various workflow components like action nodes and logic nodes. A hands-on demonstration details setting up a webhook trigger and appending data to Google Sheets.
This section explains LLM app development as the process of building applications utilizing large language models to understand, generate, and process human language, covering tasks like question answering, summarization, and content creation. It introduces LangChain as a powerful open-source framework that simplifies LLM integration with real-world data, tools, and external systems. The benefits of LangChain, such as easier LLM integration, automation of multi-step tasks, data retrieval, memory handling, decision-making agents, and tool integration, are discussed.
This part delves into the core components of LangChain: models (smart programs for understanding and generating language), prompts (input instructions for models), and output parsers (tools for structuring model-generated text). A practical demonstration illustrates how to use OpenAI APIs with LangChain, manage API keys securely, introduce style to responses, and handle various prompt templates for text processing. Code examples show how to format messages and interpret model outputs, including the use of ResponseSchema and StructuredOutputParser for structured data extraction.
This module focuses on how to implement memory in LangChain models for coherent conversations. It covers setting up environment variables and loading LLM models based on current dates. The core of the section demonstrates using LangChain's `ConversationBufferMemory` and `ConversationBufferWindowMemory` to store and manage conversation history. Code examples illustrate how to save, load, and retrieve context, allowing the chatbot to remember previous interactions and build on them, making conversations feel more natural.
This lesson explains 'chains' in LangChain as sequences of steps or components that work together to accomplish a specific task. It highlights how chains organize tasks, create clear workflows, and help scale complex operations by linking different parts like models, memory, and tools. The process of input, processing, modification/storage, and output within a chain is detailed. Code examples demonstrate using `LLMChain` and `SimpleSequentialChain` to create multi-step processes, such as generating company names and product descriptions based on user input, including a multi-prompt router chain for dynamic task routing.
This section introduces Question Answering (QA) as a natural language processing task for developing models that understand and respond to questions. It emphasizes the power of QA systems in providing instant, accurate answers and enhancing user experience. The process of QA in LangChain is outlined, from user questions to prompt templates, language model processing, and output generation. Practical code examples demonstrate setting up a QA system using `CSVLoader`, `VectorStoreIndexCreator`, and `RetrievalQA` to answer questions based on a catalog, including defining test data points and evaluating the system's performance.
This module explores building intelligent agents using LangChain. It covers setting up the project environment with necessary imports and handling model versions. The tutorial demonstrates initializing agents with various tools like `LLM Math` and `Wikipedia` to perform tasks such as mathematical calculations and information retrieval. Code examples illustrate how to process natural language queries ('What is 25% of 300?') and complex questions ('Which book did Tom M. write?'). It also shows how to define and use custom tools, like a 'time' tool, within the agent framework to get current dates, including advanced debugging techniques.
This tutorial introduces Hugging Face as a tool for working with language and AI models, particularly its 'transformers' library. It covers three key applications: speech to text recognition using Librosa and WAV2Vec2 models, sentiment analysis using pre-trained pipelines for classifying text as positive or negative, and text generation from prompts or existing datasets like poems. Code demonstrations guide through installing libraries, loading models, processing audio, and generating text, showcasing the power and ease of use of Hugging Face for various NLP tasks.
This section dives into LSTMs (Long Short-Term Memory networks), a type of recurrent neural network (RNN) designed to capture long-term dependencies in sequential data. It explains the limitations of traditional RNNs and how LSTMs overcome them using memory cells and three types of gates: input, forget, and output. applications of LSTMs in video analysis, language simulation, time series prediction, and sentiment analysis are discussed. A detailed example illustrates how LSTMs predict the next word in a sentence by remembering relevant context and discarding irrelevant details.
This segment introduces LLM benchmarks as standardized tools for evaluating the performance of large language models. It explains why benchmarks are important for tracking model progress, guiding fine-tuning, and objective comparison. The systematic process of how LLM benchmarks work (setup, testing in few-shot, zero-shot, or fine-tune modes, and scoring) is detailed. Key metrics like accuracy, recall, F1 score, exact match, perplexity, and BLEU are discussed. Limitations of benchmarking, such as bounded scoring and overfitting, are also covered. Finally, LLM leaderboards are introduced as rankings of models based on benchmark scores, providing insights into top-performing models in various categories like reasoning, coding, speed, and affordability.
This section reviews several AI tools designed to help individuals ace job interviews. Tools like Skilora.ai, Visco, Interviewing.io, Mockmate, ChatGPT, My Interview Practice, and Udei are presented, detailing their features, pricing, and how they assist in mock interviews, providing instant feedback, performance reports, and tailored coaching. The broader benefits of AI tools in interview preparation, such as company research, question practice, personalized advice, and skill refinement, are also discussed, emphasizing consistent use and external feedback for optimal results.
This segment poses 15 thought-provoking questions about machine learning, challenging understanding of core concepts. Questions cover topics like what it means for a machine to 'learn,' whether AI truly understands, the impact of incorrect data, building AI without explicit rules, distinguishing smartness from memorization, overfitting, activation functions, learning from random data, gradient descent, the importance of data size and quality, the difference between models and algorithms, model failure in real-world scenarios, loss functions, 100% accuracy, and biases in AI. Each question is accompanied by a brief explanation of its underlying challenge or implication.