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Whirlwind Tour of Python

To tap into the power of Python’s open data science stack—including NumPy, Pandas, Matplotlib, Scikit-learn, and other tools—you first need to understand the syntax, semantics, and patterns of the Python language. This report provides a brief yet comprehensive introduction to Python for engineers, researchers, and data scientists who are already familiar with another programming language.

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Python Data Science Handbook

Many researchers consider Python a first-class tool due to its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all: IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

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Machine Learning with Scikit-Learn: Introduction to Machine Learning

Get up to speed on the basics of ML and how to apply it in scikit-learn, a popular package that contains a collection of tools for machine learning written in Python. You’ll gain an understanding of the data layout expected in scikit-learn and the essential features of scikit-learn’s API, as well as how they can be used together to gain insight from a more complex dataset.

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Machine Learning with Scikit-Learn: Trees and Forests

Get a conceptual introduction to decision trees and random forests, which can be used to fit complicated datasets without overfitting. You’ll also learn about the term bagging and what it means in a machine learning context and explore examples of decision trees and random forests in scikit-learn.

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Machine Learning with Scikit-Learn: Support Vector Machines

Take an in-depth look at one of the most powerful classical machine learning techniques: support vector machines (SVMs). You’ll gain a qualitative understanding of the SVM model, an understanding of the concept of kernel methods in machine learning, and see how SVMs can be implemented and used in scikit-learn.

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Machine Learning with Scikit-Learn: Feature Engineering

Dive into examples of engineering features used in machine learning algorithms, including one-hot encoding for categorical data, frequency-based encoding for textual data, histogram-of-gradient (HOG) features for image data, and more. With this approach, you’ll better understand several common approaches to feature engineering and gain exposure to more examples of Scikit-learn’s API applied to real-world datasets.

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Machine Learning with Scikit-Learn: Hyperparameters and Model Validation

Get a primer on the concept of model validation and model selection in scikit-learn and learn how to apply a ML model to a set of data with known labels in order to evaluate how well the model works. You’ll also discuss holdout sets, cross-validation, bias and variance, and validation curves.

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