# Data Science Project List

## 1. Machine Learning

### 1.1 Core ML Libraries
- [scikit-learn](https://scikit-learn.org/): Fundamental machine learning library in Python

### 1.2 Automated Machine Learning
- [AutoML](https://www.automl.org/automl/): Overview of AutoML concepts and applications
- [AutoGluon](https://auto.gluon.ai/stable/index.html): AutoML toolkit for deep learning models

### 1.3 Feature Engineering
- Tools:
- [Feature Engine](https://feature-engine.trainindata.com/)
- [Featuretools](https://www.featuretools.com/)
- Resource: [Feature Engineering Tools](https://neptune.ai/blog/feature-engineering-tools)

### 1.4 ML Model Interpretation & Explainability
- Tools:
- [LIME](https://github.com/marcotcr/lime)
- [SHAP](https://github.com/shap/shap)
- [Mlxtend](https://rasbt.github.io/mlxtend/)
- [InterpretML](https://interpret.ml/)
- Resources:
- [ML Model Interpretation Tools](https://neptune.ai/blog/ml-model-interpretation-tools)
- [Explainability & Auditability in ML](https://neptune.ai/blog/explainability-auditability-ml-definitions-techniques-tools)
- [Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/)

### 1.5 Hyperparameter Optimization
- Tools:
- [Ray Tune](https://docs.ray.io/en/latest/tune/index.html)
- [Scikit-optimize](https://scikit-optimize.github.io/stable/index.html)
- [OPTUNA](https://optuna.org/)
- [Hyperopt](https://hyperopt.github.io/hyperopt/)
- [BayesianOptimization](https://bayesian-optimization.github.io/BayesianOptimization/1.5.1/)
- [GPyOpt](https://sheffieldml.github.io/GPyOpt/)
- [SigOpt](https://sigopt.org/)
- Resources:
- [Best Tools for Model Tuning and Hyperparameter Optimization](https://neptune.ai/blog/best-tools-for-model-tuning-and-hyperparameter-optimization)
- [Hyperparameter Tuning in Python: Complete Guide](https://neptune.ai/blog/hyperparameter-tuning-in-python-complete-guide)
- [Optuna Guide](https://neptune.ai/blog/optuna-guide-how-to-monitor-hyper-parameter-optimization-runs)
- [Optuna vs. Hyperopt](https://neptune.ai/blog/optuna-vs-hyperopt)

### 1.6 Model Quality Testing
- Tools:
- [Deepchecks](https://deepchecks.com/)
- [Kolena](https://docs.kolena.com/)
- Resource: [Tools for ML Model Testing](https://neptune.ai/blog/tools-ml-model-testing)

### 1.7 Boosting Algorithms
- Tools:
- [CatBoost](https://catboost.ai/)
- [XGBoost](https://xgboost.readthedocs.io/)
- [LightGBM](https://lightgbm.readthedocs.io/)
- Resources:
- [When to Choose CatBoost Over XGBoost or LightGBM](https://neptune.ai/blog/when-to-choose-catboost-over-xgboost-or-lightgbm)
- [XGBoost vs. LightGBM](https://neptune.ai/blog/xgboost-vs-lightgbm)
- [XGBoost: Everything You Need to Know](https://neptune.ai/blog/xgboost-everything-you-need-to-know)

### 1.8 Kernel Methods
- Tool: [CodPy](https://github.com/johnlem/codpy_alpha)
- Resource: [CodPy: a Python library for numerics, machine learning, and statistics](https://arxiv.org/abs/2402.07084)

## 2. Time Series Analysis

### 2.1 Time Series Forecasting
- Tools:
- [Skforecast](https://skforecast.org)
- [Nixtla](https://www.nixtla.io/)
- [Prophet](https://facebook.github.io/prophet/)
- [GluonTS](https://auto.gluon.ai/stable/tutorials/timeseries/index.html)
- Book: ["Forecasting: principles and practice"](https://OTexts.com/fpp3) by Hyndman & Athanasopoulos

### 2.2 Anomaly Detection
- Tool: [PyOD](https://pyod.readthedocs.io/en/latest/)
- Resources:
- [Anomaly Detection Resources](https://github.com/yzhao062/anomaly-detection-resources)
- [How to Use Python for Anomaly Detection](https://dataheadhunters.com/academy/how-to-use-python-for-anomaly-detection-in-data-detailed-steps/)

### 2.3 Change Point Detection
- Tools:
- [ruptures](https://centre-borelli.github.io/ruptures-docs/)
- [Kats](https://facebookresearch.github.io/Kats/)
- Resources:
- [Change Point Detection In Time Series With Python](https://forecastegy.com/posts/change-point-detection-time-series-python/)
- [A Brief Introduction to Change Point Detection using Python](https://techrando.com/2019/08/14/a-brief-introduction-to-change-point-detection-using-python/)

## 3. Computer Vision

### 3.1 Object Detection
- Tools:
- [ImageAI](https://imageai.readthedocs.io/)
- [GluonCV](https://cv.gluon.ai/)
- [Detectron2](https://ai.meta.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)
- Resource: [Object Detection Algorithms and Libraries](https://neptune.ai/blog/object-detection-algorithms-and-libraries)

### 3.2 Image Processing
- Tools:
- [OpenCV](https://opencv.org/)
- [Pillow (PIL Fork)](https://pillow.readthedocs.io/en/stable)
- Resources:
- [Image Processing in Python](https://neptune.ai/blog/image-processing-python)
- [Image Processing Python Libraries](https://neptune.ai/blog/image-processing-python-libraries-for-machine-learning)
- [PIL Image Tutorial](https://neptune.ai/blog/pil-image-tutorial-for-machine-learning)

### 3.3 Graph Neural Networks (GNN)
- Tools:
- [PyG](https://pytorch-geometric.readthedocs.io/)
- [DGL](https://neptune.ai/blog/graph-neural-networks-libraries-tools-learning-resources)
- [Graph Nets](https://github.com/google-deepmind/graph_nets)

## 4. Natural Language Processing (NLP)

### 4.1 NLP Libraries
- Tools:
- [NLTK](https://www.nltk.org/)
- [pyLDAvis](https://pyldavis.readthedocs.io/)
- [Wordcloud](https://amueller.github.io/word_cloud/index.html)
- [AdaptNLP](https://novetta.github.io/adaptnlp)
- [Flair](https://flairnlp.github.io/)
- [Snorkel](https://www.snorkel.org/)
- [GluonNLP](https://github.com/dmlc/gluon-nlp)
- [spaCY](https://spacy.io/)

### 4.2 Exploratory Data Analysis for NLP
- Resources:
- [EDA for NLP Tools](https://neptune.ai/blog/exploratory-data-analysis-natural-language-processing-tools)
- [Sentiment Analysis in Python](https://neptune.ai/blog/sentiment-analysis-python-textblob-vs-vader-vs-flair)
- [pyLDAvis Topic Modelling](https://neptune.ai/blog/pyldavis-topic-modelling-exploration-tool-that-every-nlp-data-scientist-should-know)

### 4.3 Large Language Models (LLMs)
- Repo: [LLM101n](https://github.com/karpathy/LLM101n)
- Resources:
- [A Very Gentle Introduction to Large Language Models without the Hype](https://mark-riedl.medium.com/a-very-gentle-introduction-to-large-language-models-without-the-hype-5f67941fa59e)
- [LLM Training: RLHF and Its Alternatives](https://magazine.sebastianraschka.com/p/llm-training-rlhf-and-its-alternatives)
- [Prompt Engineering: Classification of Techniques and Prompt Tuning](https://medium.com/the-modern-scientist/prompt-engineering-classification-of-techniques-and-prompt-tuning-6d4247b9b64c)
- [Automated Prompt Engineering: The Definitive Hands-on Guide](https://medium.com/towards-data-science/automated-prompt-engineering-the-definitive-hands-on-guide-1476c8cd3c50)

## 5. Optimization

### 5.1 Convex Optimization
- Tool: [CVXPY](https://www.cvxpy.org/)
- Book: [Convex Optimization](https://web.stanford.edu/~boyd/cvxbook/) by Boyd and Vandenberghe
- Tutorials:
- [CVXPY Tutorial](https://youtu.be/kXqu-TqEl7Q?si=FqTy3758kXgZhmHH)
- [Solving Sudoku with CVXPY](https://youtu.be/USaishDES9s?si=WPBcALQt1Nh5aJxm)

### 5.2 Genetic Algorithms
- Tool: [PyGAD](https://pygad.readthedocs.io/)
- Resource: [Train PyTorch Models Using Genetic Algorithm with PyGAD](https://neptune.ai/blog/train-pytorch-models-using-genetic-algorithm-with-pygad)
- Book: [Introduction to Evolutionary Computing](https://link.springer.com/book/10.1007/978-3-662-44874-8) by Eiben & Smith

### 5.3 Portfolio Optimization
- Tools:
- [skfolio](https://skfolio.org/)
- [pyportfolioopt](https://pyportfolioopt.readthedocs.io/)

## 6. Reinforcement Learning (RL)
- Tool: [Gym](https://gymnasium.farama.org/)
- Resources:
- [The Best Tools for Reinforcement Learning in Python](https://neptune.ai/blog/the-best-tools-for-reinforcement-learning-in-python)
- [AI Reinforcement Learning with OpenAI's Gym](https://levelup.gitconnected.com/ai-reinforcement-learning-with-openais-gym-ead06726663a)

## 7. Other Topics

### 7.1 Bayesian Data Analysis
- Tools:
- [PyMC](https://www.pymc.io/welcome.html)
- [ArviZ](https://www.arviz.org/en/latest/)
- [Bambi](https://bambinos.github.io/bambi/)
- [NumPyro](https://num.pyro.ai/en/stable/index.html)
- Book: [Bayesian Data Analysis](http://www.stat.columbia.edu/~gelman/book/) by Gelman et al.

### 7.2 Causal Machine Learning
- Tool: [CasualML](https://causalml.readthedocs.io/en/latest/index.html)
- Books:
- [Python Causality Handbook](https://matheusfacure.github.io/python-causality-handbook/landing-page.html)
- [Causal Inference: What If](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/)

### 7.3 Probabilistic Machine Learning
- Repo: [pyprobml](https://github.com/probml/pyprobml)
- Books:
- ["Probabilistic Machine Learning" - a book series](http://probml.ai/) by Kevin Murphy
- [Bayesian Reasoning and Machine Learning](http://www.cs.ucl.ac.uk/staff/d.barber/brml/) by David Barber

### 7.4 Recommender Systems
- Tools:
- [Suprise](https://surpriselib.com/)
- [Recommenders](https://github.com/recommenders-team/recommenders)
- [RecTools](https://github.com/MobileTeleSystems/RecTools)
- Tutorial: [Practical Guide to Building Scalable Recommender Systems in Python](https://medium.com/@anilcogalan/practical-guide-to-building-scalable-recommender-systems-in-python-b175547e6fce)

### 7.5 Symbolic Regression
- Tools: [PySR](https://astroautomata.com/PySR/)
- Tutorials:
- [A tutorial on simulation-based inference](https://astroautomata.com/blog/simulation-based-inference/)
- [Powerful ‘Machine Scientists’ Distill the Laws of Physics From Raw Data](https://www.quantamagazine.org/machine-scientists-distill-the-laws-of-physics-from-raw-data-20220510/)