12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
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🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
A High-Quality Real Time Upscaler for Anime Video
FinRL®: Financial Reinforcement Learning. 🔥
The "Python Machine Learning (1st edition)" book code repository and info resource
Dive into this repository, a comprehensive resource covering Data Structures, Algorithms, 450 DSA by Love Babbar, Striver DSA sheet, Apna College DSA Sheet, and FAANG Questions! 🚀 That's not all! We've got Technical Subjects like Operating Systems, DBMS, SQL, Computer Networks, and Object-Oriented Programming, all waiting for you.
PRML algorithms implemented in Python
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
Performance analysis of predictive (alpha) stock factors
Quantitative analysis, strategies and backtests
Algorithms for outlier, adversarial and drift detection
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
Use evolutionary algorithms instead of gridsearch in scikit-learn
Environment for reinforcement-learning algorithmic trading models
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.
A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.