Machine Learning for Finance in Python

Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.

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4 Hours15 Videos59 Exercises19,955 Learners
5150 XP

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Course Description

Time series data is all around us; some examples are the weather, human behavioral patterns as consumers and members of society, and financial data. In this course, you'll learn how to calculate technical indicators from historical stock data, and how to create features and targets out of the historical stock data. You'll understand how to prepare our features for linear models, xgboost models, and neural network models. We will then use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. You will also learn how to evaluate the performance of the various models we train in order to optimize them, so our predictions have enough accuracy to make a stock trading strategy profitable.

  1. 1

    Preparing data and a linear model

    Free

    In this chapter, we will learn how machine learning can be used in finance. We will also explore some stock data, and prepare it for machine learning algorithms. Finally, we will fit our first machine learning model -- a linear model, in order to predict future price changes of stocks.

    Play Chapter Now
    Machine learning for finance
    50 xp
    Explore the data with some EDA
    100 xp
    Correlations
    100 xp
    Data transforms, features, and targets
    50 xp
    Create moving average and RSI features
    100 xp
    Create features and targets
    100 xp
    Check the correlations
    100 xp
    Linear modeling
    50 xp
    Create train and test features
    100 xp
    Fit a linear model
    100 xp
    Evaluate our results
    100 xp

Datasets

NASDAQ: AAPLNASDAQ: AMDQQQ ETFSPYLNGSMLV

Collaborators

David CamposChester IsmayShon Inouye
Nathan George Headshot

Nathan George

Assistant Professor of Data Science at Regis University

I teach and develop data science courses for Regis University's Master's in data science degree. I also do research with neural networks on EEG data. I spend some of my extra time applying neural nets to financial data in order to predict future prices of stocks and cryptocurrencies.
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What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA