Learn ML: Algorithms, Applications & Resources
20 entriesCore concepts, algorithms and where machine learning is applied.
01Machine Learning ModelsLogistic regression, random forests, CatBoost, XGBoost and tabular foundation models such as TabPFN-3.5.
02Machine Learning AlgorithmsA reference guide to core ML algorithms — from linear regression and decision trees to SVMs, clustering, and ensemble methods.
03Machine Learning ApplicationsReal-world applications of machine learning across healthcare, finance, retail, manufacturing, and other industry verticals driving transformation.
04AI & Machine Learning ResourcesCurated AI explainers, machine learning tutorials and courses, and official AI learning tracks for self-directed learners.
05Deep LearningDeep learning frameworks, datasets and graph neural networks.
06Reinforcement LearningReinforcement learning hub with guides and algorithms.
07Reinforcement Learning AlgorithmsValue-based, policy-based and actor-critic algorithms, plus RLHF, GRPO, offline RL and libraries.
08Federated Learning GuideGuide to federated learning.
09Graph Neural NetworksGraph neural networks: learning from connected data.
10Top Machine Learning JournalsTop machine learning journals and publication venues.
11Data MiningClassification, clustering, association rules and anomaly detection, with software, datasets, books and conferences.
12Tabular Foundation Models (TFMs)Pretrained models for tabular data, such as TabPFN, and how they compare with gradient boosting.
13Time Series Foundation ModelsPretrained forecasting models for time series data.
14Time Series Forecasting ModelsStatistical, machine learning and deep learning models for forecasting.
15Causal Machine LearningCausal inference and discovery methods that go beyond correlation.
16Tabular Foundation Models for Data Science AgentsHow agents call TabPFN, TabICL and TabFM as tools.
17Graph & Causal Representation LearningGraph and causal representation learning resources.
18Neuro-Symbolic AICombining neural learning with symbolic reasoning.
19Spiking Neural NetworksLibraries, training methods and datasets for SNNs.
20Reinforcement Learning from Human Feedback (RLHF)RLHF papers, PPO, DPO, RLAIF, libraries, preference datasets and reward model benchmarks.
Frameworks, Libraries & Platforms
11 entriesWhere models get built: frameworks, language libraries and end-to-end platforms.
21Machine Learning PlatformsEnd-to-end platforms for building, training, deploying, and managing ML models at scale across cloud and on-premises environments.
22Machine Learning FrameworksCore libraries and frameworks powering ML model development — covering TensorFlow, PyTorch, scikit-learn, XGBoost, and beyond.
23Machine Learning LibrariesComprehensive reference to ML libraries across Python, R, Java, Scala, and other languages commonly used in research and production ML systems.
24Deep Learning FrameworksFrameworks for building and training deep neural networks.
25Federated Learning FrameworksFrameworks for federated learning across distributed data.
26JavaScript Machine Learning LibrariesML libraries for JavaScript and Node.js — bringing model training and inference directly to the browser, server, and edge environments.
27Machine Learning Frameworks in JuliaML frameworks and libraries available in the Julia programming language — valued for its high-performance numerical computing and Python-like developer experience.
28Python Libraries for Data ScientistsPython libraries for data science work.
29No-Code ML PlatformsDrag-and-drop and low-code platforms that let non-developers build, evaluate, and deploy ML models without writing code.
30AutoML ToolsTools that automate the ML pipeline — from feature engineering and model selection to hyperparameter tuning and neural architecture search.
31Data Science PlatformsPlatforms for data science workflows, from Dataiku to KNIME.
Data & Features
6 entriesDatasets, feature engineering and the stores that feed models.
32Data Sets for ML/DLCurated public and commercial datasets for training, fine-tuning, and benchmarking machine learning and deep learning models across domains.
33Feature Store SolutionsCentralized repositories for storing, sharing, and serving ML features — enabling reuse across teams and consistent training-serving parity.
34ML Feature Engineering ToolsTools for transforming raw data into informative features that improve machine learning model accuracy, generalisability, and performance.
35ML Metadata Store SolutionsSolutions for storing and querying ML metadata — experiment parameters, metrics, model versions, artifacts, and lineage information.
36Feature Engineering: Approaches & ToolsTechniques for imputation, encoding, scaling, selection and domain features, with libraries, feature stores and data quality tools.
37Data Quality & Testing FrameworksOpen frameworks for testing data quality.
Training, Tuning & Pipelines
5 entriesAutomating hyperparameter search, workflows and continuous training.
38ML HyperParameter Optimization ToolsTools for automating the search for optimal model hyperparameters — covering grid search, random search, Bayesian optimization, and neural architecture search.
39ML Workflow Orchestration ToolsTools for orchestrating and automating multi-step ML pipelines — from data preparation and training through evaluation and production deployment.
40Continuous Machine Learning (CML) FrameworksFrameworks for applying CI/CD principles to ML — automating model retraining, evaluation, and deployment in response to data and code changes.
41AI Model Optimization TechniquesPruning, quantization and distillation.
42Evaluation Metrics for Deep LearningClassification, regression and generative metrics.
MLOps, Deployment & Serving
6 entriesOperationalising models: platforms, lifecycle management and serving.
43MLOps PlatformsPlatforms that operationalise machine learning — covering experiment tracking, model deployment, pipeline automation, and lifecycle management.
44Open Source MLOps PlatformsOpen source tools for MLOps workflows — including Kubeflow, MLflow, DVC, Feast, and other community-driven solutions for model operations.
45ModelOps PlatformsPlatforms for operationalising AI/ML models at enterprise scale — covering deployment, lifecycle management, governance, versioning, and monitoring.
46ML Model Serving ToolsTools and infrastructure for serving ML models in production — covering batch inference, real-time scoring, and model deployment at scale.
47LLMOpsOperating LLM applications in production.
48Deployment Strategies for Deep LearningServing and rolling out deep learning models.
Monitoring & Observability
4 entriesWatching models in production for drift, degradation and fairness.
49ML Model Monitoring ToolsTools for monitoring deployed ML models for performance degradation, data drift, concept drift, and fairness issues in real-time.
50Explainable AI (XAI) FrameworksSHAP, LIME, InterpretML, Captum, Alibi and other tools for explaining model behavior.
51Data ObservabilityMonitoring data freshness, quality and lineage.
52AI Explainability TechniquesExplainability techniques, tools and libraries.
Courses & Certifications
4 entriesLearning paths for machine learning and related fields.
53Machine Learning Courses and CertificationsMaths, core ML, deep learning, reinforcement learning and MLOps.
54AI Courses and CertificationsMachine learning, generative AI and vendor AI certifications.
55Free AI CoursesFree AI courses from top universities.
56Courses and Certifications HubAll courses and certifications guides on this site.
Related Topics
5 entriesNeighbouring AI and data directories.
57Natural Language ProcessingNatural language processing tools and models.
58Computer VisionComputer vision and pose-estimation models.
59Data ScienceData science hub.
60StatisticsFree textbooks and courses, open data, software such as R and statsmodels, societies and journals.
61Reinforcement Learning GuideFrom MDPs to deep reinforcement learning.
Textbooks & Courses
8 entriesFree machine learning references.
62scikit-learn User GuideClassical machine learning in Python.
63Elements of Statistical LearningFree statistical learning textbook.
64Google Machine Learning Crash CourseFree introductory ML course.
65Kaggle LearnShort free data science courses.
66Stanford CS229Machine learning course.
67Dive into Deep LearningFree interactive deep learning textbook with code.
68Made With MLHands-on guide to designing, developing, deploying and iterating production ML.
69Full Stack Deep LearningCourse on building and shipping deep learning applications.
Frequently Asked Questions
What is machine learning?
Machine learning is a branch of AI in which algorithms learn patterns from data to make predictions or decisions without being explicitly programmed for each case. Typical tasks include classification, regression, clustering and recommendation.
What is MLOps?
MLOps applies DevOps practices to machine learning: versioning data and models, automating training and deployment pipelines, and monitoring models in production so they stay accurate. The MLOps and Open Source MLOps pages list the platforms.
What is the difference between a framework and a platform?
A framework such as PyTorch or scikit-learn is a library you use to write and train models. A platform adds managed infrastructure, experiment tracking, deployment and governance around those frameworks.
Why do teams use a feature store?
A feature store keeps one governed copy of the features used for training and serving, so models see consistent inputs in both places and teams can reuse features instead of rebuilding them.
What should I monitor once a model is deployed?
Track prediction quality, input data drift, concept drift, latency and fairness. The monitoring and observability pages list tools that alert when a model begins to degrade.