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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.
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.
Explore four methods to quantify fat tails using Python, including power law tail index, kurtosis, log-normal's σ, and Taleb's κ. Apply these techniques to real-world social media data analysis.
Learn to detect power laws in real-world data using Python, covering log-log and maximum likelihood approaches with practical examples using artificial and social media datasets.
Explore power laws, fat tails, and their impact on statistical analysis. Learn why traditional methods fall short and gain insights into analyzing extreme events in various domains.
Explore the key steps in building a foundation LLM, from data curation to evaluation, with insights on architecture, training at scale, and design choices for large language models.
Learn to fine-tune large language models with practical Python examples. Explore techniques like LoRA for efficient parameter tuning and implement a text classification model using distilBERT.
Master prompt engineering techniques to enhance AI problem-solving. Learn 7 key tricks and explore practical applications using LangChain for building AI-powered solutions.
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.
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