Jeff Heaton Courses

Introduction to Python and Google Colab for Data Science and Machine Learning

1 rating

Quick Python and Google CoLab tutorial for data science and machine learning, covering basics, file operations, and environment setup for beginners familiar with other programming languages.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Programming LSTM with Keras and TensorFlow

1 rating

Explore LSTM and GRU layers for recurrent neural networks in Keras, laying the groundwork for NLP and time series prediction with practical examples and implementations.

• YouTube
• 28 minutes
• Self-Paced
• Free Video

Question Answering over Documents with RAG - Lecture 6.4

Explore Retrieval-Augmented Generation (RAG) to enhance LLMs with external data, improving accuracy and relevance in corporate settings. Learn to set up and query a RAG system using sample employee data.

• YouTube
• 10 minutes
• Self-Paced
• Free Video

Introduction to Kaggle for Data Science Competitions - Lecture 8.1

Learn to navigate Kaggle competitions, understand public and private leaderboards, and prepare for a custom dataset assignment using Large Language Models in this data science and machine learning guide.

• YouTube
• 12 minutes
• Self-Paced
• Free Video

Introduction to Retrieval-Augmented Generation (RAG) - Lecture 6.1

Explore Retrieval-Augmented Generation (RAG) for precise document-based Q&A using LLMs and LangChain. Learn about retrieval, augmentation, and generation phases to enhance model accuracy.

• YouTube
• 10 minutes
• Self-Paced
• Free Video

Handling Revision Prompts - Iterative Code Generation with LLMs

Explore interactive code generation with LLMs, focusing on refining prompts for PyTorch neural networks. Learn to guide AI for better results and adhere to coding standards.

• YouTube
• 10 minutes
• Self-Paced
• Free Video

Prompting for Code Generation - Leveraging LLMs for Efficient Programming

Learn to leverage AI for efficient code generation using OpenAI's LLMs. Explore practical examples, from simple Fibonacci sequences to complex neural networks, and discover how to integrate AI tools into your programming workflow.

• YouTube
• 11 minutes
• Self-Paced
• Free Video

Generative AI Overview - Foundations, Models, and Applications

Explore foundational concepts of Generative AI, its evolution, and key models like DALL·E and GPT. Learn how these technologies reshape creativity and innovation across various industries.

• YouTube
• 11 minutes
• Self-Paced
• Free Video

Kaggle Notebooks - Introduction to Cloud-Based Data Science Platforms

Explore Kaggle notebooks for seamless data access, free GPU usage, and efficient machine learning model development without complex local setups.

• YouTube
• 13 minutes
• Self-Paced
• Free Video

Introduction to Building Web Applications with Streamlit - Part 10.2

Learn how to build interactive web applications for generative AI using Streamlit, from basic setup to creating user-friendly interfaces with Python scripts and AI model integration.

• YouTube
• 13 minutes
• Self-Paced
• Free Video

Running StreamLit in Google Colab - Building Interactive Web Applications for Generative AI

Dive into building interactive web applications for generative AI using Streamlit. Learn to create dynamic, reactive apps with Python, exploring layout, widgets, and state management techniques.

• YouTube
• 11 minutes
• Self-Paced
• Free Video

Applications of Deep Neural Networks with PyTorch - Module 1.1

Discover how PyTorch deep neural networks revolutionize machine learning through practical applications, emphasizing their unique advantages and implementation in modern AI solutions.

• YouTube
• 14 minutes
• Self-Paced
• Free Video

Applications of Generative AI - Part 1.1

Master practical applications of Generative AI through hands-on experience with LLMs, text-to-image networks, and RAG models using Python, LangChain, and OpenAI's tools for innovative AI solutions.

• YouTube
• 11 minutes
• Self-Paced
• Free Video

Applications of Deep Neural Networks - 2016 Version

Dive into deep learning fundamentals through recorded university lectures covering neural network applications and practical implementations.

• YouTube
• 23 hours 23 minutes
• Self-Paced
• Free Video

Introduction to Neural Networks for Java

Master neural network programming fundamentals in Java through hands-on implementation of algorithms, backpropagation, genetic algorithms, and real-world applications.

• YouTube
• 6 hours 48 minutes
• Self-Paced
• Free Video