此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

Code for Machine Learning for Algorithmic Trading, 2nd edition.

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

Interview = 简历指南 + 算法题 + 八股文 + 源码分析

Performance analysis of predictive (alpha) stock factors

This github repository of "Machine Learning and Data Science Blueprints for Finance". Please star.

Code for the online course "Deployment of Machine Learning Models"

Use evolutionary algorithms instead of gridsearch in scikit-learn

Code and shared files

Amazon Textract Code Samples

Practical Time-Series Analysis, published by Packt

Tutorials on Causal Inference and pgmpy

Jupyter Notebooks and code for the book Artificial Intelligence in Finance (O'Reilly) by Yves Hilpisch.

A repository for all ZenML projects that are specific production use-cases.

Stock price prediction with recurrent neural network. The data is from the Chinese stock.

Important paper implementations for Question Answering using PyTorch

Hands-On Data Science for Marketing, published by Packt

All projects and lecture notes of the Udacity Machine Learning Engineer Nanodegree.

Python package of actuarial models, tools, examples and learning materials.

Building ETL Pipelines with Python

From scratch Python implementation of the fast ICA algorithm.

Immersion of the world of Finance through Python

Individual household electric-power consumption Data Set (LSTM) [tutorial]

Bandit algorithms simulations for online learning

Summary of the Kaggle Stock Prediction Competition & my Trial

A tutorial for the Great Expectations library.

Create an end-to-end AI solution that will help predict insurance premium cost with IBM Watson Studio and AutoAI.

Implementation of the DDPG algorithm for Optimal Finance Trading