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Explore the Hugging Face Transformers library for easy, cost-free use of open-source language models. Learn sentiment analysis, summarization, and build a chatbot UI with Gradio.
Discover how multi-agent systems overcome single-agent limitations through four key architectures: independent, decentralized, centralized, and hybrid approaches for AI applications.
Explore AI's business value through a non-technical lens. Learn key concepts, practical applications, and five essential rules for leveraging AI effectively in your organization.
Learn Python basics for AI development through a hands-on guide. Master data types, variables, scripts, loops, functions, and libraries. Build a research paper summarizer to apply your skills.
Explore three LLM compression techniques: quantization, pruning, and knowledge distillation. Learn to implement these methods with Python code examples for efficient model optimization.
Explore three practical AI applications for analyzing sales data, demonstrated with real-world examples from a warm outreach campaign. Learn data augmentation, structuring unstructured data, and lead scoring techniques.
Fine-tune a local LLM on M1 Mac using MLX and QLoRA. Adapt Mistral 7b to respond like you to YouTube comments, with step-by-step guidance and code examples.
Automate data pipelines using Python and GitHub Actions. Learn to create ETL scripts, set up repositories, configure workflows, and implement ML applications for efficient data processing.
Deploy ML solutions using FastAPI, Docker, and AWS with a simple 3-step approach. Learn to create APIs, containerize applications, and deploy on cloud platforms for efficient machine learning deployment.
Develop a semantic search tool for YouTube videos using Python, focusing on experimentation, evaluation, and UI building in machine learning solutions.
Learn to build data pipelines for machine learning projects using Python. Explore ETL vs ELT, extraction, transformation, loading, and orchestration. Includes practical example of processing YouTube video transcripts.
Explore text embeddings for classification and semantic search with Python. Learn to transform text into computable vectors for powerful NLP applications.
Learn to enhance LLMs using Retrieval Augmented Generation (RAG). Explore its principles, implementation, and practical application in improving a fine-tuned model for YouTube comment responses.
Learn to fine-tune large language models on a single GPU using QLoRA. Explore quantization techniques, LoRA, and practical implementation with Python code for custom YouTube comment responses.
Explore three methods to create custom AI assistants using OpenAI: GPTs, Assistants API, and Fine-tuning API. Learn to build a YouTube comment responder through practical demonstrations.
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