https://github.com/code-alchemist01/machine-learning-kingdom-open-souce

Bu açık kaynaklı depo, Yapay Zeka (YZ) alanına ilgi duyanlar için kapsamlı bir rehber niteliği taşıyor. Proje, özellikle Yapay Zeka ajanları oluşturma üzerine derinlemesine bir kılavuz ve bu alandaki eğitim, araçlar, haberler, topluluklar ve veri setleri gibi çeşitli kaynakları bir araya getiriyor.
https://github.com/code-alchemist01/machine-learning-kingdom-open-souce

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Bu açık kaynaklı depo, Yapay Zeka (YZ) alanına ilgi duyanlar için kapsamlı bir rehber niteliği taşıyor. Proje, özellikle Yapay Zeka ajanları oluşturma üzerine derinlemesine bir kılavuz ve bu alandaki eğitim, araçlar, haberler, topluluklar ve veri setleri gibi çeşitli kaynakları bir araya getiriyor.

• Host: GitHub
• URL: https://github.com/code-alchemist01/machine-learning-kingdom-open-souce
• Owner: code-alchemist01
• License: mit
• Created: 2025-06-03T14:27:18.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-10-07T09:40:57.000Z (6 months ago)
• Last Synced: 2025-10-15T20:57:37.810Z (5 months ago)
• Homepage:
• Size: 220 KB
• Stars: 27
• Watchers: 0
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 🧠 Machine Learning Kingdom — Ultimate AI Learning Map

A **complete open-source AI education map** — from **beginner to expert** — covering every essential domain of Artificial Intelligence.

Each domain below is split into: 🎥 **Courses/Playlists**, 📘 **Books**, 📰 **Papers/Articles/Blogs**, 🧠 **Repos/Frameworks**, 🗂️ **Datasets**, 🏁 **Benchmarks/Leaderboards**, 🛠️ **Tools/Platforms/Services**, 🧪 **Tutorials/Notebooks**.

**Preferences:** Free-first; best-in-class paid mixed in. English first; TR when strong.

---

## 🧭 Recommended Learning Path

1) Mathematics & Programming Foundations  

2) Machine Learning Basics  

3) Deep Learning  

4) NLP & Computer Vision  

5) Reinforcement Learning  

6) LLM Engineering / RAG  

7) MLOps & Deployment  

8) Specialized (XAI, Federated/Edge, Graph/Time-Series, Causal, Recommenders, Multimodal, Audio)  

9) Domains (Medical/Finance/Geo/Robotics)  

10) Ethics, Safety & Governance

---

## 🌍 Machine Learning (ML)

**Levels:** Beginner → Intermediate → Advanced → Expert

### 🎥 Courses / Playlists

- "Andrew Ng – Machine Learning (Coursera)" = "https://www.coursera.org/learn/machine-learning"

- "Google – Machine Learning Crash Course" = "https://developers.google.com/machine-learning/crash-course"

- "fast.ai – Intro to ML for Coders" = "http://course18.fast.ai/ml.html"

- "Udacity – Intro to Machine Learning" = "https://www.udacity.com/course/intro-to-machine-learning--ud120"

- "Caltech – Learning from Data (Yaser Abu-Mostafa)" = "https://www.youtube.com/playlist?list=PLnIDYuXHkit4LcWjDe0EwlE57WiGlBs08"

- "MITx – MicroMasters SDS (free audit)" = "https://micromasters.mit.edu/ds/"

- "Microsoft – Data Science for Beginners (YouTube)" = "https://www.youtube.com/playlist?list=PLlrxD0HtieHi5c9-iFZqH2fQ0e4Jd2mB_"

- "Kaggle Learn – Machine Learning" = "https://www.kaggle.com/learn/intro-to-machine-learning"

### 📘 Books

- "An Introduction to Statistical Learning (ISL)" = "https://www.statlearning.com/"

- "The Elements of Statistical Learning (ESL)" = "https://hastie.su.domains/ElemStatLearn/"

- "Pattern Recognition and Machine Learning (Bishop)" = "https://www.springer.com/gp/book/9780387310732"

- "Probabilistic ML: An Introduction (Murphy, free draft)" = "https://probml.github.io/pml-book/book1.html"

- "Probabilistic ML: Advanced Topics (Murphy)" = "https://probml.github.io/pml-book/book2.html"

### 📰 Papers / Articles / Blogs

- "Google – Rules of ML" = "https://developers.google.com/machine-learning/guides/rules-of-ml"

- "Distill.pub – Visual Essays" = "https://distill.pub/"

- "Lilian Weng – Blog" = "https://lilianweng.github.io/"

- "Bias-Variance Tradeoff (UW CSE notes)" = "https://courses.cs.washington.edu/courses/cse546/16au/bias-variance.pdf"

- "Feature Scaling/Engineering (KDnuggets guide)" = "https://www.kdnuggets.com/2020/04/feature-engineering-scaling.html"

### 🧠 Repos / Frameworks

- "scikit-learn" = "https://scikit-learn.org/stable/"

- "XGBoost" = "https://github.com/dmlc/xgboost"

- "LightGBM" = "https://github.com/microsoft/LightGBM"

- "CatBoost" = "https://github.com/catboost/catboost"

- "Microsoft – ML for Beginners" = "https://github.com/microsoft/ML-For-Beginners"

### 🗂️ Datasets

- "UCI Machine Learning Repository" = "https://archive.ics.uci.edu/"

- "OpenML" = "https://www.openml.org/"

- "Kaggle Datasets" = "https://www.kaggle.com/datasets"

- "Google Dataset Search" = "https://datasetsearch.research.google.com/"

### 🏁 Benchmarks / Leaderboards

- "OpenML Tasks/Benchmarks" = "https://www.openml.org/search?type=task"

- "Papers with Code – SOTA by Task" = "https://paperswithcode.com/"

- "MLPerf (MLCommons)" = "https://mlcommons.org/en/"

### 🛠️ Tools / Platforms

- "Google Colab" = "https://colab.research.google.com/"

- "Optuna (HPO)" = "https://optuna.org/"

- "Weights & Biases" = "https://wandb.ai/"

- "Neptune.ai (tracking)" = "https://neptune.ai/"

### 🧪 Tutorials / Notebooks

- "scikit-learn Tutorials" = "https://scikit-learn.org/stable/tutorial/"

- "Kaggle Learn – Intermediate ML" = "https://www.kaggle.com/learn/intermediate-machine-learning"

- "Feature Engineering Cookbook (GitHub)" = "https://github.com/PacktPublishing/Feature-Engineering-Cookbook"

---

## 🤖 Deep Learning

### 🎥 Courses / Playlists

- "fast.ai – Practical DL for Coders" = "https://course.fast.ai/"

- "DeepLearning.AI – Deep Learning Specialization" = "https://www.coursera.org/specializations/deep-learning"

- "MIT 6.S191 – Intro to DL" = "https://introtodeeplearning.com/"

- "Karpathy – Neural Networks: Zero to Hero" = "https://karpathy.ai/zero-to-hero.html"

- "NYU – Yann LeCun Lectures" = "https://www.youtube.com/@yannlecture"

- "Stanford CS230 (archive + slides)" = "https://cs230.stanford.edu/"

### 📘 Books

- "Deep Learning (Goodfellow, Bengio, Courville)" = "https://www.deeplearningbook.org/"

- "Dive into Deep Learning" = "https://d2l.ai/"

- "Neural Networks and Deep Learning (Nielsen)" = "http://neuralnetworksanddeeplearning.com/"

- "Probabilistic Deep Learning (site)" = "https://www.probabilistic-deeplearning.org/"

### 📰 Papers (Classics)

- "ResNet" = "https://arxiv.org/abs/1512.03385"

- "Batch Normalization" = "https://arxiv.org/abs/1502.03167"

- "Attention Is All You Need" = "https://arxiv.org/abs/1706.03762"

- "Adam Optimizer" = "https://arxiv.org/abs/1412.6980"

- "Dropout" = "https://jmlr.org/papers/v15/srivastava14a.html"

### 🧠 Repos / Frameworks

- "PyTorch" = "https://pytorch.org/"

- "TensorFlow / Keras" = "https://www.tensorflow.org/"

- "JAX" = "https://github.com/google/jax"

- "Flax" = "https://github.com/google/flax"

- "Lightning" = "https://lightning.ai/"

### 🗂️ Datasets

- "ImageNet" = "https://image-net.org/"

- "CIFAR-10/100" = "https://www.cs.toronto.edu/~kriz/cifar.html"

- "Tiny ImageNet" = "https://www.kaggle.com/c/tiny-imagenet"

- "SVHN" = "http://ufldl.stanford.edu/housenumbers/"

### 🏁 Benchmarks

- "MLPerf – Training/Inference" = "https://mlcommons.org/en/"

- "DAWNBench (historic)" = "https://dawn.cs.stanford.edu/benchmark/"

### 🛠️ Tools

- "NVIDIA CUDA Toolkit" = "https://developer.nvidia.com/cuda-toolkit"

- "PyTorch AMP (Mixed Precision)" = "https://pytorch.org/docs/stable/amp.html"

- "ONNX Runtime" = "https://onnxruntime.ai/"

- "TensorRT" = "https://developer.nvidia.com/tensorrt"

### 🧪 Tutorials

- "PyTorch Tutorials" = "https://pytorch.org/tutorials/"

- "Keras Examples" = "https://keras.io/examples/"

- "D2L Notebooks" = "https://github.com/d2l-ai/d2l-en"

---

## 🧩 Natural Language Processing (NLP)

### 🎥 Courses / Playlists

- "Stanford CS224n: NLP with Deep Learning" = "https://www.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ"

- "DeepLearning.AI – NLP Specialization" = "https://www.coursera.org/specializations/natural-language-processing"

- "fast.ai – Code-First NLP" = "https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9"

- "Hugging Face – NLP/LLM Course" = "https://huggingface.co/learn/llm-course"

### 📘 Books

- "Speech and Language Processing (3e draft)" = "https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf"

- "NLTK Book" = "https://www.nltk.org/book/"

- "Foundations of Statistical NLP (Manning & Schütze)" = "https://nlp.stanford.edu/fsnlp/"

### 📰 Papers / Articles

- "BERT" = "https://arxiv.org/abs/1810.04805"

- "GPT-3" = "https://arxiv.org/abs/2005.14165"

- "T5" = "https://arxiv.org/abs/1910.10683"

- "LoRA" = "https://arxiv.org/abs/2106.09685"

- "UL2" = "https://arxiv.org/abs/2205.05131"

### 🧠 Repos / Frameworks

- "Transformers (HF)" = "https://github.com/huggingface/transformers"

- "SentenceTransformers" = "https://www.sbert.net/"

- "spaCy" = "https://spacy.io/"

- "AllenNLP" = "https://allenai.org/allennlp"

- "OpenNMT" = "https://opennmt.net/"

### 🗂️ Datasets

- "GLUE" = "https://gluebenchmark.com/"

- "SuperGLUE" = "https://super.gluebenchmark.com/"

- "SQuAD" = "https://rajpurkar.github.io/SQuAD-explorer/"

- "WikiText" = "https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/"

- "The Pile" = "https://pile.eleuther.ai/"

- "OSCAR" = "https://oscar-project.github.io/"

### 🏁 Benchmarks

- "MTEB" = "https://huggingface.co/spaces/mteb/leaderboard"

- "HELM" = "https://crfm.stanford.edu/helm/latest/"

- "BLEU/METEOR (refs)" = "https://acl.mit.edu/resources/papers/bleu"

### 🛠️ Tools

- "OpenAI Cookbook" = "https://github.com/openai/openai-cookbook"

- "HF Tokenizers" = "https://github.com/huggingface/tokenizers"

- "spaCy Projects/Templates" = "https://github.com/explosion/projects"

### 🧪 Tutorials

- "Hugging Face – LLM Course" = "https://huggingface.co/learn/llm-course"

- "spaCy Course" = "https://course.spacy.io/"

- "HF Datasets Guide" = "https://huggingface.co/docs/datasets/index"

---

## 👁️ Computer Vision (CV)

### 🎥 Courses / Playlists

- "Stanford CS231n" = "https://www.youtube.com/playlist?list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk"

- "CU Boulder – Computer Vision Specialization" = "https://www.coursera.org/specializations/computer-vision-cu"

- "fast.ai – Vision" = "https://course.fast.ai/"

- "MIT 6.819/6.869 – Adv. CV (archive)" = "http://6.869.csail.mit.edu/"

### 📘 Books

- "Szeliski – Computer Vision: Algorithms and Applications" = "https://szeliski.org/Book/"

- "Multiple View Geometry (Hartley & Zisserman)" = "http://www.robots.ox.ac.uk/~vgg/hzbook/"

### 📰 Papers

- "Mask R-CNN" = "https://arxiv.org/abs/1703.06870"

- "ViT (Vision Transformer)" = "https://arxiv.org/abs/2010.11929"

- "Segment Anything" = "https://arxiv.org/abs/2304.02643"

- "DETR" = "https://arxiv.org/abs/2005.12872"

### 🧠 Repos / Frameworks

- "OpenCV" = "https://opencv.org/"

- "Ultralytics YOLO" = "https://github.com/ultralytics/ultralytics"

- "Detectron2" = "https://github.com/facebookresearch/detectron2"

- "MMDetection" = "https://github.com/open-mmlab/mmdetection"

- "OpenMMLab Family" = "https://openmmlab.com/"

### 🗂️ Datasets

- "COCO" = "https://cocodataset.org/"

- "Pascal VOC" = "http://host.robots.ox.ac.uk/pascal/VOC/"

- "Open Images" = "https://storage.googleapis.com/openimages/web/index.html"

- "Cityscapes" = "https://www.cityscapes-dataset.com/"

- "KITTI" = "http://www.cvlibs.net/datasets/kitti/"

### 🏁 Benchmarks

- "COCO SOTA (PwC)" = "https://paperswithcode.com/sota/object-detection-on-coco"

- "ADE20K SOTA" = "https://paperswithcode.com/sota/semantic-segmentation-on-ade20k"

### 🛠️ Tools

- "Roboflow" = "https://roboflow.com/"

- "FiftyOne" = "https://voxel51.com/fiftyone/"

- "Weights & Biases – CV Reports" = "https://wandb.ai/site"

### 🧪 Tutorials

- "PyImageSearch" = "https://pyimagesearch.com/"

- "HF Vision Course" = "https://huggingface.co/learn/computer-vision-course"

- "Detectron2 Colabs" = "https://github.com/facebookresearch/detectron2/tree/main/projects"

---

## 🎮 Reinforcement Learning (RL)

### 🎥 Courses / Playlists

- "David Silver – RL (UCL/DeepMind)" = "https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ"

- "Berkeley CS285 – Deep RL" = "https://rail.eecs.berkeley.edu/deeprlcourse/"

- "Hugging Face – Deep RL Course" = "https://huggingface.co/learn/deep-rl-course"

- "OpenAI Spinning Up (lectures/notes)" = "https://spinningup.openai.com/"

### 📘 Books

- "Sutton & Barto – RL: An Introduction" = "https://incompleteideas.net/book/the-book-2nd.html"

- "Algorithms for RL (Szepesvári)" = "https://sites.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf"

### 📰 Papers

- "DQN" = "https://www.nature.com/articles/nature14236"

- "PPO" = "https://arxiv.org/abs/1707.06347"

- "SAC" = "https://arxiv.org/abs/1801.01290"

- "IMPALA" = "https://arxiv.org/abs/1802.01561"

- "DreamerV3" = "https://arxiv.org/abs/2301.04104"

### 🧠 Repos / Frameworks

- "Gymnasium" = "https://www.gymlibrary.dev/"

- "Stable-Baselines3" = "https://github.com/DLR-RM/stable-baselines3"

- "CleanRL" = "https://github.com/vwxyzjn/cleanrl"

- "RLlib (Ray)" = "https://docs.ray.io/en/latest/rllib/"

- "Acme (DeepMind)" = "https://github.com/deepmind/acme"

### 🗂️ Datasets

- "D4RL (Offline RL)" = "https://github.com/Farama-Foundation/D4RL"

- "Atari / DM Control / MuJoCo" = "https://www.gymlibrary.dev/"

### 🏁 Benchmarks

- "RL Baselines3 Zoo Leaderboards" = "https://github.com/DLR-RM/rl-baselines3-zoo"

- "BSuite (DeepMind)" = "https://github.com/deepmind/bsuite"

### 🛠️ Tools

- "WandB RL Sweeps" = "https://docs.wandb.ai/guides/sweeps"

- "Hydra (config mgmt)" = "https://github.com/facebookresearch/hydra"

### 🧪 Tutorials

- "Spinning Up – Practical Exercises" = "https://spinningup.openai.com/en/latest/spinningup/rl_intro.html"

- "SB3 – Colab Tutorials" = "https://colab.research.google.com/github/araffin/rl-tutorial-jnrr19/blob/sb3/"

---