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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 Exercises21,719 Learners5150 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


    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.

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    Machine learning for finance
    50 xp
    Explore the data with some EDA
    100 xp
    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




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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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Lloyds Banking Group

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Harvard Business School

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