Python-tutorial

This article covers how and when to use random forest classification with scikit-learn, focusing on concepts, workflow, and examples. We also cover how to use the confusion matrix and feature importances.

Importing data is the first step in any data science project. Learn why today's data scientists prefer the pandas read_csv() function to do this.

In this tutorial, you'll learn about absolute and weighted word frequency in text mining and how to calculate it with defaultdict and pandas DataFrames.

Discover the Python pickle module: learn about serialization, when (not) to use it, how to compress pickled objects, multiprocessing, and much more!

In this tutorial, you'll learn more about autoencoders and how to build convolutional and denoising autoencoders with the notMNIST dataset in Keras.

Learn the fundamentals of object-oriented programming in Python, including classes, objects, inheritance, encapsulation, and polymorphism.

Learn how you can parse, explore, modify and populate XML files with the Python ElementTree package, for loops and XPath expressions.

A step-by-step guide to easily deploying a Facebook Messenger chatbot with Python, using Flask, requests and ngrok.

Analyze poker hands with Python and easily implement statistical concepts such as combinations, permutations, (in)dependent events and expected value.

Learn about Python tuples: what they are, how to create them, when to use them, what operations you perform on them and various functions you should know.

Learn to set up a data science environment on Google Cloud: create an instance on Google Compute Engine, install Anaconda and run Jupyter notebooks!

In this tutorial, data science meets Search Engine Marketing: learn to create Google AdWords campaigns by generating keywords and ad templates with Python!