L11 – Overview of Machine Learning & SciKitLearn in Python

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L11 – Overview of Machine Learning & SciKitLearn in Python

This webpage covers some of the basics of setting up Python for Machine Learning
Most of the specifics of creating and using Machine Learning is covered under the following sections. Go back to the Python home page to access the links to these pages.

L12 – Association Rules
L13 – Classifications
L13-1 – KNN
L13-2 – Naive Bayes
L13-3 – Decisions Trees
L13-4 – Regression
L13-5 – SVM
L13-6 – Random Forests
L14 – Neural Networks
L15 – Clustering
L15-1 – KMeans

1. What Python library do you need
To do machine learning using Python there are a number of librarys available. You may have some of these already installed from following the previous sections in this Python course. Check back to the home page and the various links to other topics.

The most commonly used Python libraries for Data Science projects are listed below under two sections. The first section is a general list of Python libraries for exploring and working with your data sets. The second section lists the machine learning librarys.

  • Exploring and working with data set libraries
    • Pandas
    • Plotly
    • Matplotlib
    • SciPy
    • NumPy
    • Seaborn
  • Machine Learning libraries
    • Statsmodels
    • Scikit-Learn
    • ensorFlow
    • NLKT – for natural language processing

The most commonly used  machine learning library is Scikit-Learn. This will be the main focus for most of the machine learning  algorithms and examples.

2. The Scikit-Learn library
Scikit-learn is a free software machine learning library for the Python programming language. It various machine learning algorithms to allow you to perform classification, regression and clustering algorithms, and includes support vector machines, random forests, gradient boosting, k-means and DBSCAN. It is built upon some other Python libraries including NumPy and SciPy.

SciKit-Learn Library

The SciKit-Learn webpage comes with lots and lots of example of using the various machine learning, and other, algorithms.
The documentation and tutorials on their website is very comprehensive.

3. Install Scikit-Learn library
The following command illustrates how to install the SciKet-Learn library in your Python environment.  During the installation it will check what other libraries you have installed and will, if required, download and install any additionally required packages.

pip3 install scikit-learn

Collecting scikit-learn

  Using cached

Installing collected packages:

Successfully installed

4. Other resources
SciKet-Learn library website

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