Free Reinforcement Learning Learning Path

Fast-paced introduction to machine learning using TensorFlow. Covers essential ML concepts with hands-on exercises and real-world examples.

This tutorial introduces Neuro-Symbolic AI as a method to enhance Large Language Models (LL Ms). It focuses on making LL Ms more robust, explainable, and instructable by combining symbolic knowledge structures with statistical learning techniques. The goal is to address the limitations of black-box LL Ms, particularly in terms of transparency and domain-specific protocol understanding.

A free course with over 50 theoretical lessons and 10 practical projects, teaching how to train, fine-tune, and deploy LL Ms into AI products. It covers SFT, RLHF, LoRA, and custom model training.

This program includes modules on data exploration and statistical inference, where students perform statistical analysis on real-world datasets and build and validate hypotheses using statistical tests.

This program blends ICF-accredited coaching mastery with digital fluency, empowering coaches to lead with confidence in a world where human insight and AI innovation go hand in hand.

This course equips learners with the strategies and tools to design, manage, and scale AI projects in real-world environments. It emphasizes applying agile methodologies and risk mitigation to optimize AI initiatives.

This course provides a comprehensive introduction to attention mechanisms and the transformer models that are foundational to modern GenAI systems. It covers self-attention, multi-head attention, and the overall transformer architecture, with real-world demos.

This course explores the use of computer simulations, particularly agent-based models, to study social science theories. You will learn how to grow and study artificial societies to understand and improve the real world.

A comprehensive introduction to the mathematical principles that form the foundation of artificial intelligence and machine learning, bridging essential concepts with real-world AI applications.

This course equips you with the skills to analyze, implement, and assess large language models in real-world scenarios. You will learn about core LLM capabilities, summarization, translation, and how LL Ms power content generation. The course also covers building chatbots and sentiment analysis tools with LangChain and evaluating LLM performance using benchmarks like ROUGE, GLUE, and BIG-bench.

This course is designed for scientists, engineers, and other problem-solvers who want to learn the basics of statistical thinking and how to apply it to real-world problems. You will learn about data analysis, experimental design, and statistical modeling.

A comprehensive program designed to prepare you for the field of artificial intelligence and machine learning. It covers designing scalable AI & ML infrastructure, core algorithms, AI agent development, and leveraging cloud-based AI & ML services, specifically through Microsoft Azure. A capstone project simulates real-world challenges.

A one-day immersive workshop led by Andrew Jones, the creator of data contracts. The course delves into the transformative world of Data Contracts and how to use them to implement a Data Mesh. It is tailored for software, platform, and data engineers looking for practical guidance on implementing data contracts and data mesh in their organizations.

In partnership with AMD, this course teaches how to apply fine-tuning and reinforcement learning to improve LLM behavior, reasoning, and safety. You will learn about the post-training lifecycle, core techniques like RLHF and LoRA, and how to design evaluations to detect issues like reward hacking and diagnose failures.

This course covers the fundamentals of unsupervised learning techniques such as clustering and dimensionality reduction to make sense of large, unlabeled datasets. You will learn to implement k-means and hierarchical clustering.

This course teaches the fundamentals and principal AI concepts about clustering, dimensionality reduction, reinforcement learning, and deep learning to solve real-life problems. Students will learn the basics of several machine learning topics to help solve real-life challenges, including unsupervised learning techniques such as clustering and dimensionality reduction.

This course, part of the TinyML Professional Certificate series, delves into the practical applications of Tiny Machine Learning. Students explore the code behind widely used TinyML applications like keyword spotting, visual wake words, and anomaly detection. The course uses real-world industry applications to illustrate the principles of TinyML.

This course provides an introduction to the foundations of data, strategy, and analytical tools like AI and Machine Learning as they apply to real estate. It includes lessons on machine learning, guest lectures from Columbia University professors, and a group project to design a real-world AI application.

Reinforcement Learning Specialization

Advanced Writing with Grammarly AI