AI for Beginners - AI Basics, Prompts, AI Development, RAG

Maximize ChatGPT and Gemini by being specific, providing context, breaking tasks down, and proofreading; leverage creative formats, multimodal inputs, and ethical use to boost creativity and productivity.

Demonstrates the ChatGPT mobile app’s voice input and conversation features while teaching e-book promotion across Facebook, Instagram, Twitter, LinkedIn, and Pinterest, plus email marketing and course vs e-book options.

Explore Google's NotebookLM, an ai tool for notes, docs, and podcasts that uploads sources and generates grounded summaries, timelines, and study guides.

Learn to use Google Gemini to access your Gmail, Docs, and Drive content by enabling the Google Workspace extension, then summarize unread emails and documents to save time.

Explore Google's Teachable Machine, a browser-based tool for training image, audio, and pose models and exporting a tensorflow.js file for use in apps.

Explore a guided overview of 2025 AI tools across chatbots, image generation, video and audio creation, coding, productivity, and research, with practical use cases and cautions about accuracy.

Explore Google AI Studio, a browser-based platform to explore Google Gemini and open-source models. Copy code in Python, JavaScript, Go, or curl, and obtain an API key to test prompts.

Discover Google Gemini Gems, a gem manager that lets you use pre-made or custom prompts to automate coding, emails, and knowledge files in replies, saving time.

Explore how to create a multiple-choice quiz using google gemini for general knowledge, including prompts, html formatting, and verifying an answer key.

Discover how to leverage Google Gemini to earn money online by content creation, blogs, ebooks, web development, affiliate marketing, and YouTube, with prompts, cross-checking, and best practices.

Learn to use Google Gemini for promoting products and services on Twitter, creating tweets and images, and leveraging hashtags with careful prompts and review.

Learn digital marketing with AI by using ChatGPT, Gemini, DeepSeek, and Qwen to brainstorm, write articles, create images, and run campaigns, while applying prompt engineering and noting limitations.

ChatGPT uses the GPT-4o multimodal model in chatgpt.com to generate marketing images, from promos to infographics. It highlights effective prompts and free usage limits for digital and affiliate marketers.

Explore how Python accelerates AI development with key libraries like NumPy, pandas, scikit-learn, TensorFlow, and transformers, and leverage notebooks, Colab, and data processing for effective model building.

Learn to generate local subtitles with OpenAI's Whisper by converting video to wav with ffmpeg, installing the Whisper package, transcribing in Python, and exporting to .srt or .vtt.

Explore how to train a simple TensorFlow ai model using a dataset on Google Colab, leveraging Keras, MNIST, and basic steps from loading data to evaluating accuracy.

Differentiate machine learning from deep learning by data labeling and neural networks; ML handles structured data with rules, while DL learns from unstructured data using layered networks powering language models.

Explore Scikit-Learn, a Python library for traditional machine learning, including classification, regression, clustering, and pre-processing. Learn to train, test, and evaluate models with iris data using simple workflows.

Explore Hugging Face to access thousands of models, datasets, and spaces for AI development. Use the transformer library and pipeline to run and test models in Google Colab or locally.

deploy a simple ai chatbot for wellness and fitness using the gemini model with streamlit on hugging face spaces, upload app.py and requirements, set api key secret.

Explore TensorFlow Playground to learn deep learning with browser-based experiments and datasets like Gaussian, spiral, and xor. Tune learning rate, activation, regularization, and hidden layers while monitoring loss.

Explain retrieval-augmented generation (rag) by combining retrieval and generation with external knowledge; present the rag workflow, embeddings, vector database, knowledge sources, applications, advantages, limitations, and a demo.

Learn to build a simple AI app using the Google Gemini API by obtaining an API key, installing the library, and creating a basic chatbot with Streamlit.

Build ai chatbots powered by large language models using prompt engineering, context memory, and tool calls. Deploy with Streamlit UI and LangChain memory on Hugging Face.

Explore computer vision fundamentals, including image formats, OpenCV preprocessing, and real-time object detection with YOLO. Learn how to prepare datasets (Roboflow, data.yaml), train models, and perform inference with best.pt.

Explains the model context protocol (MCP) and JSON RPC 2.0, showing how MCP servers and clients connect tools for context-rich LLM workflows, and demonstrates a calculator with add and multiply.

Explore LangGraph's multi-agent system, memory and human-in-the-loop capabilities, demonstrated in a simple demo of a state graph with nodes and edges for agentic AI orchestration.

discover ai agents as llm-powered systems that observe, reason, and act to automate tasks using tools and memory, demonstrated with a LangChain based weather and calculator demo.

Context engineering expands prompt engineering by architecting memory, external data, tools, and conversations to enable ai agents with up-to-date, personalized, multi-step reasoning through rag and tool integration.