All Projects → JannesKlaas → MLiFC

JannesKlaas / MLiFC

Licence: MIT license
Course Material for the machine learning in financial context bootcamp

Programming Languages

Jupyter Notebook
11667 projects

Projects that are alternatives of or similar to MLiFC

empirical-methods
Homepage for 17-803 "Empirical Methods" at Carnegie Mellon University
Stars: ✭ 103 (+0.98%)
Mutual labels:  course-materials
66DaysOfData
#66DaysOfData challenge in Financial Machine Learning and NLP
Stars: ✭ 21 (-79.41%)
Mutual labels:  finance
pybacen
This library was developed for economic analysis in the Brazilian scenario (Investments, micro and macroeconomic indicators)
Stars: ✭ 40 (-60.78%)
Mutual labels:  finance
stockholm
💵 Modern Python library for working with money and monetary amounts. Human friendly and flexible approach for development. 100% test coverage + built-in support for GraphQL and Protocol Buffers transports using current best-practices.
Stars: ✭ 26 (-74.51%)
Mutual labels:  finance
techindicators
Python functions and an associated Jupyter notebook for technical analysis of stock price data. Numpy is used for calculating technical indicators. Matplotlib and mpl_finance are used for plotting data.
Stars: ✭ 31 (-69.61%)
Mutual labels:  finance
query2report
Query2Report is a simple open source business intelligence platform that allows users to build report/dashboard for business analytics or enterprise reporting
Stars: ✭ 43 (-57.84%)
Mutual labels:  business-intelligence
tradestation-python-api
A Python Client library for the TradeStation API.
Stars: ✭ 69 (-32.35%)
Mutual labels:  finance
priceR
Economics and Pricing in R
Stars: ✭ 32 (-68.63%)
Mutual labels:  finance
harlan
Harlan é o sistema modular que permite você automatizar toda sua governança cadastral da nuvem.
Stars: ✭ 25 (-75.49%)
Mutual labels:  business-intelligence
awesome-open-finance
A curated list of open finance and open banking resources
Stars: ✭ 92 (-9.8%)
Mutual labels:  finance
optlib
A library for financial options pricing written in Python.
Stars: ✭ 166 (+62.75%)
Mutual labels:  finance
cap-table-tool
Cap Table and Exit Waterfall Tool, https://foresight.is/cap-table
Stars: ✭ 22 (-78.43%)
Mutual labels:  finance
ap-monorepo
Monorepo containing all packages related to the ACTUS Protocol
Stars: ✭ 15 (-85.29%)
Mutual labels:  finance
fin
finance
Stars: ✭ 38 (-62.75%)
Mutual labels:  finance
esc101-18-19-a
ESC 101A: Fundamentals of Computing (C Programming Course), IIT Kanpur, 2018-19-autumn offering
Stars: ✭ 23 (-77.45%)
Mutual labels:  course-materials
vogon-java
Vogon - A simple personal finance tracker using Spring Boot and AngularJS
Stars: ✭ 16 (-84.31%)
Mutual labels:  finance
fints-institute-db
Database of German Banks and their HBCI / FinTS endpoints
Stars: ✭ 28 (-72.55%)
Mutual labels:  finance
knut
knut is an efficient plain text accounting tool with support for multiple currencies and valuation.
Stars: ✭ 40 (-60.78%)
Mutual labels:  finance
ledgeraccounting
Simple web-based accounting and budgeting tool built upon the popular accounting software, hledger
Stars: ✭ 28 (-72.55%)
Mutual labels:  finance
ark-invest-api
📈 API for tracking holdings and trades of ARK Invest funds
Stars: ✭ 49 (-51.96%)
Mutual labels:  finance

Welcome to MLiFC

Hi! You found the MLiFC GitHub repository. What is MLiFC you ask? MLiFC is short for machine learning in financial context, a new textbook that aims to teach business, economics and social science students practical machine learning and its applications in business and finance. It is written for the MLiFC bootcamp, taught at turing society rotterdam, also known as 'Bletchley'.

You can apply to the Bletchley bootcamp taught in Rotterdam from February to April 2018 here. For students who have recieved an invitation to complete the Python challenge, you can find it here

The content here is mostly developed by Jannes Klaas with the support of a community of technical and non-technical people. You are welcome to join us.

Table of content

This ToC gets updated as new chapters come online:

It is intended as a jumping board into ML & AI, a basis from which students can dive deeper into specific topics that interest them.

What is this repository?

In this repository we are developing all the course material, free and open for everyone. There is not much good material to help business folk learn about machine learning. So we set out to create our own! The bootcamp that we are developing this material for aims to teach business and finance majors the basics of ML, and especially deep learning, over the course of eight weeks. The theoretical content is not disseminated by lecture, but through iPython notebooks. They are interactive and students can immediately mess around with the code examples provided. The heart of the course are weekly challenges, much like kaggle competitions. Here students gain a lot of practical understanding. As you might have noticed, the content in this repository is unfinished. This is on purpose, as we want to develop the content in the open and under the scrutiny of the community. We believe we can create better content that way.

I found a mistake / something is not clear, what do I do?

File an issue! We love people finding mistakes, so if you find an issue please let us know by filing an issue on GitHub. Even better, if you know a fix, fix it and send a pull request!

Can I use this for my classes?

Yes of course! The content is licensed under an MIT license, meaning you can use it for your class as well. We'd love to hear from you and share knowledge, so just shoot an email to jannes [at] tsociety.io to get in touch.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].