scikit-learn-machine-learning-tutorial

Following along with Sentdex’s tutorial

This follows along with the tutorial: Scikit-learn Machine Learning with Python and SKlearn.

How to use Scikit-learn (sklearn) with the python programming language to do Machine Learning with Support Vector Machines. Covered specifically here, we learn how to use Linear SVC to see if we can determine, based on fundamental information, whether a stock is likely to outperform the market or not.

The accompanying website can be found here.

Parts

Part 1 - Intro to Machine Learning with Scikit Learn and Python

Intro to a practical example of Machine Learning with the Python programming language and the Scikit-learn, or sklearn, module. We're covering an example with investing, where we use machine learning to discern fundamental characteristics of companies that perform well over a long term period.
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Part 2 - Simple Support Vector Machine (SVM) example with character recognition

Show you simple machine learning can actually be, where the real hard part is actually getting data, labeling data, and organizing the data. To emphasize this, we're going to use a pre-existing data set that comes with of Scikit-Learn.
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Part 3 - Our Method and where we will be getting our Data

Cover how to acquire, label and organize data, as well as figure out which machine learning algorithm to use.
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Part 4 - Parsing data

How to handle our data set for machine learning. Cover basic code regarding how to pull specific data points out of the file.
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Part 5 - More Parsing

Pulling out the specific data point that we're interested in as using as a feature. Video | Text

Part 6 - More Parsing

Use the Pandas module to help structure and modify our data.
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Part 7 - Getting more data and meshing data sets

Grab S&P 500 index data to use as a benchmark. Label stocks that outperform market or not.
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Part 8 - Labeling of data part 1

Label data using the stock price's performance compared to the S&P 500 index's performance.
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Part 9 - Labeling of data part 2

Calculate the difference in percent change performance between the individual stocks and the overall S&P 500 index.
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Part 10 - More Parsing

Labeling data as out or under-performing the S&P500.
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Part 11 - Linear SVC Machine learning SVM example with Python

Basic linear SVC example with scikit-learn.
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Part 12 - Getting more features from our data

How to grab more data features from our data set for us to do learning on. Video | Text