Type: Tool

course material on text-mining / corpus-linguistics in German funded by the federal state of North Rhine-Westphalia

Scaler Data Science & Machine Learning Program

Data Scientist with Python

Coursera Tensorflow in practice

COMP3222/COMP6246 - Machine Learning Technologies

Keras in Motion

Oxford Deep Learning - video

OpenIntro

Free resources and learn what data science is and how it’s used in different industries.

Free Course and Certification for building an end-to-end machine using W&B

Learn about Data Science, Machine Learning, Python etc

Introduction to machine learning covering basic theory, algorithms and applications

StepByStepML - Interactive calculator that visualizes the step-by-step manual math behind machine learning algorithms for exam prep.

Understand Data Science Course in Java

12 free Data Science projects to practice Python and Pandas

Minimum Viable Study Plan for Machine Learning Interviews

Tutorials of source code from the book Genetic Algorithms with Python by Clinton Sheppard

Free cross-platform search engine indexing 50,000+ tutorials from Udemy, Skillshare, Pluralsight, and other major learning platforms across 45+ categories.

AI literature search, document translation, and deep-research workspace for researchers.

MCP server that gives AI agents access to a database of scientific papers built from raw experimental data extracted from full-text studies. Returns 25+ structured fields per paper including methods, results, sample sizes, and quality scores. GitHub

Production lessons on orchestrator-worker agents, parallel search, evaluation, and operational reliability.

A reference architecture that turns project work into isolated, observable coding-agent runs.

An open-source platform for running software development agents locally or in the cloud.

Evaluation and experimentation for retrieval and generative AI applications.

Test cases, assertions, model comparisons, and red-team checks for LLM applications.

A practical method for building task suites, graders, transcripts, and evaluation harnesses.

Modular pipelines for retrieval and generative AI applications.

Patterns for agents that make progress across multiple context windows and recover from failure.

Google's framework for developing and evaluating agents.

A small SDK for tools, handoffs, guardrails, tracing, and agent orchestration.

Low-level orchestration for long-running, stateful agents.

A vendor-neutral specification for agent discovery, task delegation, streaming, asynchronous updates, and cross-platform communication.

The open specification for connecting AI applications to external tools, data sources, prompts, and interactive apps.

Risks and mitigations for developing and deploying generative AI applications.

How to select, structure, and manage the context available to long-running agents.

Code examples for structured outputs, tool use, retrieval, evals, and other LLM application patterns.

Focused courses on current generative AI engineering techniques.

An introductory path through generative AI concepts and Google Cloud tooling.

Build language models from data preparation through evaluation and deployment.

Build neural networks and language models from first principles.

A paid program for agentic coding and building, testing, and shipping production AI systems.

Theory, intuition, and practical notebooks by Simon Prince.

A probability-grounded treatment by Christopher and Hugh Bishop.

Mathematical foundations by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

A concise, technical introduction by Andriy Burkov.

LLMOps, fine-tuning, serving, and production workflows.

A visual and practical guide by Jay Alammar and Maarten Grootendorst.

The broad reference for classical AI, including search, reasoning, planning, learning, and robotics.