├── Guide to building AI Agents from zero to production.pdf ├── LICENSE └── README.md /Guide to building AI Agents from zero to production.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/code-alchemist01/Machine-Learning-Kingdom-Open-Souce/HEAD/Guide to building AI Agents from zero to production.pdf -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2025 Kutay Şahin 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 🧠 Machine Learning Kingdom — Ultimate AI Learning Map 2 | 3 | A **complete open-source AI education map** — from **beginner to expert** — covering every essential domain of Artificial Intelligence. 4 | Each domain below is split into: 🎥 **Courses/Playlists**, 📘 **Books**, 📰 **Papers/Articles/Blogs**, 🧠 **Repos/Frameworks**, 🗂️ **Datasets**, 🏁 **Benchmarks/Leaderboards**, 🛠️ **Tools/Platforms/Services**, 🧪 **Tutorials/Notebooks**. 5 | **Preferences:** Free-first; best-in-class paid mixed in. English first; TR when strong. 6 | 7 | --- 8 | 9 | ## 🧭 Recommended Learning Path 10 | 1) Mathematics & Programming Foundations 11 | 2) Machine Learning Basics 12 | 3) Deep Learning 13 | 4) NLP & Computer Vision 14 | 5) Reinforcement Learning 15 | 6) LLM Engineering / RAG 16 | 7) MLOps & Deployment 17 | 8) Specialized (XAI, Federated/Edge, Graph/Time-Series, Causal, Recommenders, Multimodal, Audio) 18 | 9) Domains (Medical/Finance/Geo/Robotics) 19 | 10) Ethics, Safety & Governance 20 | 21 | --- 22 | 23 | ## 🌍 Machine Learning (ML) 24 | **Levels:** Beginner → Intermediate → Advanced → Expert 25 | 26 | ### 🎥 Courses / Playlists 27 | - "Andrew Ng – Machine Learning (Coursera)" = "https://www.coursera.org/learn/machine-learning" 28 | - "Google – Machine Learning Crash Course" = "https://developers.google.com/machine-learning/crash-course" 29 | - "fast.ai – Intro to ML for Coders" = "http://course18.fast.ai/ml.html" 30 | - "Udacity – Intro to Machine Learning" = "https://www.udacity.com/course/intro-to-machine-learning--ud120" 31 | - "Caltech – Learning from Data (Yaser Abu-Mostafa)" = "https://www.youtube.com/playlist?list=PLnIDYuXHkit4LcWjDe0EwlE57WiGlBs08" 32 | - "MITx – MicroMasters SDS (free audit)" = "https://micromasters.mit.edu/ds/" 33 | - "Microsoft – Data Science for Beginners (YouTube)" = "https://www.youtube.com/playlist?list=PLlrxD0HtieHi5c9-iFZqH2fQ0e4Jd2mB_" 34 | - "Kaggle Learn – Machine Learning" = "https://www.kaggle.com/learn/intro-to-machine-learning" 35 | 36 | ### 📘 Books 37 | - "An Introduction to Statistical Learning (ISL)" = "https://www.statlearning.com/" 38 | - "The Elements of Statistical Learning (ESL)" = "https://hastie.su.domains/ElemStatLearn/" 39 | - "Pattern Recognition and Machine Learning (Bishop)" = "https://www.springer.com/gp/book/9780387310732" 40 | - "Probabilistic ML: An Introduction (Murphy, free draft)" = "https://probml.github.io/pml-book/book1.html" 41 | - "Probabilistic ML: Advanced Topics (Murphy)" = "https://probml.github.io/pml-book/book2.html" 42 | 43 | ### 📰 Papers / Articles / Blogs 44 | - "Google – Rules of ML" = "https://developers.google.com/machine-learning/guides/rules-of-ml" 45 | - "Distill.pub – Visual Essays" = "https://distill.pub/" 46 | - "Lilian Weng – Blog" = "https://lilianweng.github.io/" 47 | - "Bias-Variance Tradeoff (UW CSE notes)" = "https://courses.cs.washington.edu/courses/cse546/16au/bias-variance.pdf" 48 | - "Feature Scaling/Engineering (KDnuggets guide)" = "https://www.kdnuggets.com/2020/04/feature-engineering-scaling.html" 49 | 50 | ### 🧠 Repos / Frameworks 51 | - "scikit-learn" = "https://scikit-learn.org/stable/" 52 | - "XGBoost" = "https://github.com/dmlc/xgboost" 53 | - "LightGBM" = "https://github.com/microsoft/LightGBM" 54 | - "CatBoost" = "https://github.com/catboost/catboost" 55 | - "Microsoft – ML for Beginners" = "https://github.com/microsoft/ML-For-Beginners" 56 | 57 | ### 🗂️ Datasets 58 | - "UCI Machine Learning Repository" = "https://archive.ics.uci.edu/" 59 | - "OpenML" = "https://www.openml.org/" 60 | - "Kaggle Datasets" = "https://www.kaggle.com/datasets" 61 | - "Google Dataset Search" = "https://datasetsearch.research.google.com/" 62 | 63 | ### 🏁 Benchmarks / Leaderboards 64 | - "OpenML Tasks/Benchmarks" = "https://www.openml.org/search?type=task" 65 | - "Papers with Code – SOTA by Task" = "https://paperswithcode.com/" 66 | - "MLPerf (MLCommons)" = "https://mlcommons.org/en/" 67 | 68 | ### 🛠️ Tools / Platforms 69 | - "Google Colab" = "https://colab.research.google.com/" 70 | - "Optuna (HPO)" = "https://optuna.org/" 71 | - "Weights & Biases" = "https://wandb.ai/" 72 | - "Neptune.ai (tracking)" = "https://neptune.ai/" 73 | 74 | ### 🧪 Tutorials / Notebooks 75 | - "scikit-learn Tutorials" = "https://scikit-learn.org/stable/tutorial/" 76 | - "Kaggle Learn – Intermediate ML" = "https://www.kaggle.com/learn/intermediate-machine-learning" 77 | - "Feature Engineering Cookbook (GitHub)" = "https://github.com/PacktPublishing/Feature-Engineering-Cookbook" 78 | 79 | --- 80 | ## 🤖 Deep Learning 81 | ### 🎥 Courses / Playlists 82 | - "fast.ai – Practical DL for Coders" = "https://course.fast.ai/" 83 | - "DeepLearning.AI – Deep Learning Specialization" = "https://www.coursera.org/specializations/deep-learning" 84 | - "MIT 6.S191 – Intro to DL" = "https://introtodeeplearning.com/" 85 | - "Karpathy – Neural Networks: Zero to Hero" = "https://karpathy.ai/zero-to-hero.html" 86 | - "NYU – Yann LeCun Lectures" = "https://www.youtube.com/@yannlecture" 87 | - "Stanford CS230 (archive + slides)" = "https://cs230.stanford.edu/" 88 | 89 | ### 📘 Books 90 | - "Deep Learning (Goodfellow, Bengio, Courville)" = "https://www.deeplearningbook.org/" 91 | - "Dive into Deep Learning" = "https://d2l.ai/" 92 | - "Neural Networks and Deep Learning (Nielsen)" = "http://neuralnetworksanddeeplearning.com/" 93 | - "Probabilistic Deep Learning (site)" = "https://www.probabilistic-deeplearning.org/" 94 | 95 | ### 📰 Papers (Classics) 96 | - "ResNet" = "https://arxiv.org/abs/1512.03385" 97 | - "Batch Normalization" = "https://arxiv.org/abs/1502.03167" 98 | - "Attention Is All You Need" = "https://arxiv.org/abs/1706.03762" 99 | - "Adam Optimizer" = "https://arxiv.org/abs/1412.6980" 100 | - "Dropout" = "https://jmlr.org/papers/v15/srivastava14a.html" 101 | 102 | ### 🧠 Repos / Frameworks 103 | - "PyTorch" = "https://pytorch.org/" 104 | - "TensorFlow / Keras" = "https://www.tensorflow.org/" 105 | - "JAX" = "https://github.com/google/jax" 106 | - "Flax" = "https://github.com/google/flax" 107 | - "Lightning" = "https://lightning.ai/" 108 | 109 | ### 🗂️ Datasets 110 | - "ImageNet" = "https://image-net.org/" 111 | - "CIFAR-10/100" = "https://www.cs.toronto.edu/~kriz/cifar.html" 112 | - "Tiny ImageNet" = "https://www.kaggle.com/c/tiny-imagenet" 113 | - "SVHN" = "http://ufldl.stanford.edu/housenumbers/" 114 | 115 | ### 🏁 Benchmarks 116 | - "MLPerf – Training/Inference" = "https://mlcommons.org/en/" 117 | - "DAWNBench (historic)" = "https://dawn.cs.stanford.edu/benchmark/" 118 | 119 | ### 🛠️ Tools 120 | - "NVIDIA CUDA Toolkit" = "https://developer.nvidia.com/cuda-toolkit" 121 | - "PyTorch AMP (Mixed Precision)" = "https://pytorch.org/docs/stable/amp.html" 122 | - "ONNX Runtime" = "https://onnxruntime.ai/" 123 | - "TensorRT" = "https://developer.nvidia.com/tensorrt" 124 | 125 | ### 🧪 Tutorials 126 | - "PyTorch Tutorials" = "https://pytorch.org/tutorials/" 127 | - "Keras Examples" = "https://keras.io/examples/" 128 | - "D2L Notebooks" = "https://github.com/d2l-ai/d2l-en" 129 | 130 | --- 131 | ## 🧩 Natural Language Processing (NLP) 132 | ### 🎥 Courses / Playlists 133 | - "Stanford CS224n: NLP with Deep Learning" = "https://www.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ" 134 | - "DeepLearning.AI – NLP Specialization" = "https://www.coursera.org/specializations/natural-language-processing" 135 | - "fast.ai – Code-First NLP" = "https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9" 136 | - "Hugging Face – NLP/LLM Course" = "https://huggingface.co/learn/llm-course" 137 | 138 | ### 📘 Books 139 | - "Speech and Language Processing (3e draft)" = "https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf" 140 | - "NLTK Book" = "https://www.nltk.org/book/" 141 | - "Foundations of Statistical NLP (Manning & Schütze)" = "https://nlp.stanford.edu/fsnlp/" 142 | 143 | ### 📰 Papers / Articles 144 | - "BERT" = "https://arxiv.org/abs/1810.04805" 145 | - "GPT-3" = "https://arxiv.org/abs/2005.14165" 146 | - "T5" = "https://arxiv.org/abs/1910.10683" 147 | - "LoRA" = "https://arxiv.org/abs/2106.09685" 148 | - "UL2" = "https://arxiv.org/abs/2205.05131" 149 | 150 | ### 🧠 Repos / Frameworks 151 | - "Transformers (HF)" = "https://github.com/huggingface/transformers" 152 | - "SentenceTransformers" = "https://www.sbert.net/" 153 | - "spaCy" = "https://spacy.io/" 154 | - "AllenNLP" = "https://allenai.org/allennlp" 155 | - "OpenNMT" = "https://opennmt.net/" 156 | 157 | ### 🗂️ Datasets 158 | - "GLUE" = "https://gluebenchmark.com/" 159 | - "SuperGLUE" = "https://super.gluebenchmark.com/" 160 | - "SQuAD" = "https://rajpurkar.github.io/SQuAD-explorer/" 161 | - "WikiText" = "https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/" 162 | - "The Pile" = "https://pile.eleuther.ai/" 163 | - "OSCAR" = "https://oscar-project.github.io/" 164 | 165 | ### 🏁 Benchmarks 166 | - "MTEB" = "https://huggingface.co/spaces/mteb/leaderboard" 167 | - "HELM" = "https://crfm.stanford.edu/helm/latest/" 168 | - "BLEU/METEOR (refs)" = "https://acl.mit.edu/resources/papers/bleu" 169 | 170 | ### 🛠️ Tools 171 | - "OpenAI Cookbook" = "https://github.com/openai/openai-cookbook" 172 | - "HF Tokenizers" = "https://github.com/huggingface/tokenizers" 173 | - "spaCy Projects/Templates" = "https://github.com/explosion/projects" 174 | 175 | ### 🧪 Tutorials 176 | - "Hugging Face – LLM Course" = "https://huggingface.co/learn/llm-course" 177 | - "spaCy Course" = "https://course.spacy.io/" 178 | - "HF Datasets Guide" = "https://huggingface.co/docs/datasets/index" 179 | 180 | --- 181 | 182 | ## 👁️ Computer Vision (CV) 183 | ### 🎥 Courses / Playlists 184 | - "Stanford CS231n" = "https://www.youtube.com/playlist?list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk" 185 | - "CU Boulder – Computer Vision Specialization" = "https://www.coursera.org/specializations/computer-vision-cu" 186 | - "fast.ai – Vision" = "https://course.fast.ai/" 187 | - "MIT 6.819/6.869 – Adv. CV (archive)" = "http://6.869.csail.mit.edu/" 188 | 189 | ### 📘 Books 190 | - "Szeliski – Computer Vision: Algorithms and Applications" = "https://szeliski.org/Book/" 191 | - "Multiple View Geometry (Hartley & Zisserman)" = "http://www.robots.ox.ac.uk/~vgg/hzbook/" 192 | 193 | ### 📰 Papers 194 | - "Mask R-CNN" = "https://arxiv.org/abs/1703.06870" 195 | - "ViT (Vision Transformer)" = "https://arxiv.org/abs/2010.11929" 196 | - "Segment Anything" = "https://arxiv.org/abs/2304.02643" 197 | - "DETR" = "https://arxiv.org/abs/2005.12872" 198 | 199 | ### 🧠 Repos / Frameworks 200 | - "OpenCV" = "https://opencv.org/" 201 | - "Ultralytics YOLO" = "https://github.com/ultralytics/ultralytics" 202 | - "Detectron2" = "https://github.com/facebookresearch/detectron2" 203 | - "MMDetection" = "https://github.com/open-mmlab/mmdetection" 204 | - "OpenMMLab Family" = "https://openmmlab.com/" 205 | 206 | ### 🗂️ Datasets 207 | - "COCO" = "https://cocodataset.org/" 208 | - "Pascal VOC" = "http://host.robots.ox.ac.uk/pascal/VOC/" 209 | - "Open Images" = "https://storage.googleapis.com/openimages/web/index.html" 210 | - "Cityscapes" = "https://www.cityscapes-dataset.com/" 211 | - "KITTI" = "http://www.cvlibs.net/datasets/kitti/" 212 | 213 | ### 🏁 Benchmarks 214 | - "COCO SOTA (PwC)" = "https://paperswithcode.com/sota/object-detection-on-coco" 215 | - "ADE20K SOTA" = "https://paperswithcode.com/sota/semantic-segmentation-on-ade20k" 216 | 217 | ### 🛠️ Tools 218 | - "Roboflow" = "https://roboflow.com/" 219 | - "FiftyOne" = "https://voxel51.com/fiftyone/" 220 | - "Weights & Biases – CV Reports" = "https://wandb.ai/site" 221 | 222 | ### 🧪 Tutorials 223 | - "PyImageSearch" = "https://pyimagesearch.com/" 224 | - "HF Vision Course" = "https://huggingface.co/learn/computer-vision-course" 225 | - "Detectron2 Colabs" = "https://github.com/facebookresearch/detectron2/tree/main/projects" 226 | 227 | --- 228 | 229 | ## 🎮 Reinforcement Learning (RL) 230 | ### 🎥 Courses / Playlists 231 | - "David Silver – RL (UCL/DeepMind)" = "https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ" 232 | - "Berkeley CS285 – Deep RL" = "https://rail.eecs.berkeley.edu/deeprlcourse/" 233 | - "Hugging Face – Deep RL Course" = "https://huggingface.co/learn/deep-rl-course" 234 | - "OpenAI Spinning Up (lectures/notes)" = "https://spinningup.openai.com/" 235 | 236 | ### 📘 Books 237 | - "Sutton & Barto – RL: An Introduction" = "https://incompleteideas.net/book/the-book-2nd.html" 238 | - "Algorithms for RL (Szepesvári)" = "https://sites.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf" 239 | 240 | ### 📰 Papers 241 | - "DQN" = "https://www.nature.com/articles/nature14236" 242 | - "PPO" = "https://arxiv.org/abs/1707.06347" 243 | - "SAC" = "https://arxiv.org/abs/1801.01290" 244 | - "IMPALA" = "https://arxiv.org/abs/1802.01561" 245 | - "DreamerV3" = "https://arxiv.org/abs/2301.04104" 246 | 247 | ### 🧠 Repos / Frameworks 248 | - "Gymnasium" = "https://www.gymlibrary.dev/" 249 | - "Stable-Baselines3" = "https://github.com/DLR-RM/stable-baselines3" 250 | - "CleanRL" = "https://github.com/vwxyzjn/cleanrl" 251 | - "RLlib (Ray)" = "https://docs.ray.io/en/latest/rllib/" 252 | - "Acme (DeepMind)" = "https://github.com/deepmind/acme" 253 | 254 | ### 🗂️ Datasets 255 | - "D4RL (Offline RL)" = "https://github.com/Farama-Foundation/D4RL" 256 | - "Atari / DM Control / MuJoCo" = "https://www.gymlibrary.dev/" 257 | 258 | ### 🏁 Benchmarks 259 | - "RL Baselines3 Zoo Leaderboards" = "https://github.com/DLR-RM/rl-baselines3-zoo" 260 | - "BSuite (DeepMind)" = "https://github.com/deepmind/bsuite" 261 | 262 | ### 🛠️ Tools 263 | - "WandB RL Sweeps" = "https://docs.wandb.ai/guides/sweeps" 264 | - "Hydra (config mgmt)" = "https://github.com/facebookresearch/hydra" 265 | 266 | ### 🧪 Tutorials 267 | - "Spinning Up – Practical Exercises" = "https://spinningup.openai.com/en/latest/spinningup/rl_intro.html" 268 | - "SB3 – Colab Tutorials" = "https://colab.research.google.com/github/araffin/rl-tutorial-jnrr19/blob/sb3/" 269 | 270 | --- 271 | --------------------------------------------------------------------------------