Python-For-Finance-Algorithmic-Trading-DataCamp-

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

Python-For-Finance-Algorithmic-Trading-DataCamp-

★ 8 Open GitHub ↗

No description available.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# DataCamp Community

Files related to articles written for the DataCamp Community.

## Machine Learning in R For Beginners
This machine learning tutorial for beginners was first published on the DataCamp Blog on the 25th of March 2015. It was recenlty updated on the DataCamp Community on the 11th of April 2017. You can find the article [here](https://www.datacamp.com/community/tutorials/machine-learning-in-r).

The file that is included into this repository is the original file that was written in R Markdown. The tutorial is built up around the steps that one needs to go through to elaborate a machine learning project in Python. It departs from one of the most popular data sets for machine learning, namely, the iris data set. After a short data exploration, the tutorial goes on to show how to use R to work with the well-known machine learning algorithm called “KNN” or k-nearest neighbors: you learn how to build up and evaluate your model. The KNN or k-nearest neighbors algorithm is one of the simplest machine learning algorithms and is an example of instance-based learning, where new data are classified based on stored, labeled instances (=supervised learning). 
## Python For Finance Tutorial For Beginners
A tutorial for beginners on how to get started with Python for Finance. This tutorial was published on the DataCamp Community on the 1st of June 2017. You can read the article [here](https://www.datacamp.com/community/tutorials/finance-python-trading).

The file that is included in this repository is the Jupyter Notebook that contains the code that I used to write the tutorial. The tutorial is meant to give tailored, in-depth and step-by-step information to beginners on the stock market, how to set up your programming environment, and how to get started with Python's popular data manipulation package `pandas` to do some common financial analyses first and then make a simple trading strategy. Afterwards, the article shows how to backtest the strategy with `pandas` as well as `zipline` and Quantopian, and details how you can improve and evaluate your strategy.

## Scikit-Learn Tutorial: Python Machine Learning
This scikit-learn tutorial for beginners was published on the DataCamp Community on the 3rd of January. 
You can find it [here](https://www.datacamp.com/community/tutorials/machine-learning-python).

The tutorial was written in R Markdown in combination with [DataCamp Light](https://github.com/datacamp/datacamp-light) and [Pythonwhat](https://github.com/datacamp/pythonwhat). The tutorial is built up around the steps that one needs to go through in order to elaborate a machine learning project with Python. In this case, it departs from one of the built-in data sets in scikit-learn, namely, digits, but the option of downloading the data from the UCI Machine Learning Repository is also discussed. Several models are visualized with `matplotlib` and evaluated with the appropriate scikit-learn modules in this tutorial and pointers for further machine learning/data science projects are also included.

## TensorFlow Tutorial For Beginners
A TensorFlow tutorial for beginners, first published on the DataCamp Community on the 13th of July, 2017. Read the full tutorial [here](https://www.datacamp.com/community/tutorials/tensorflow-tutorial).

The file that is included in this repository is the Jupyter Notebook that contains the code that I used to write the tutorial. The tutorial is meant as a relatively short and step-by-step guide for beginners who want to get started with Deep Learning (DL) with [TensorFlow](tensorflow.org).

Python-For-Finance-Algorithmic-Trading-DataCamp-

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README