Machine Learning for Finance in Python

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5

(3)

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

All around us, you will find time series data. These data include financial data, weather, human behavior patterns, financial data, financial data and financial data. This course will show you how to calculate technical indicators from historical stock data. Learn how to create features using historical stock data. This course will show you how to prepare features that can be used in linear, xgboost and neural network models. We will be using decision trees, random forests, and neural networks to predict the future stock prices in the US. To maximize the accuracy and effectiveness of stock trading strategies, we will need to be capable of assessing the performance of each model and evaluating its effectiveness.

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

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Supervised Learning with scikit-learn

What You Will Learn

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

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You'll understand how to prepare our features for linear models, xgboost models, and neural network models

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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

Course Instructors

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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 net...

Course Reviews

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