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Author: Guest

Random Forests (and Extremely) in Python with scikit-learn

In this guest post, you will learn by example how to do two popular machine learning techniques called random forest and extremely random forests. In fact, this post is an excerpt (adapted to the blog format) from the forthcoming Artificial Intelligence with Python – Second Edition: Your Complete Guide to Building Intelligent Apps using Python 3.x and TensorFlow 2. Now, before you will learn how to carry out random forests in Python with scikit-learn, you will find some brief information about the book.

random forest classifier in python

How to Handle Coroutines with asyncio in Python

When a program becomes very long and complex, it is convenient to divide it into subroutines, each of which implements a specific task. However, subroutines cannot be executed independently, but only at the request of the main program, which is responsible for coordinating the use of subroutines.

In this post, we introduce a generalization of the concept of subroutines, known as coroutines: just like subroutines, coroutines compute a single computational step, but unlike subroutines, there is no main program to coordinate the results. The coroutines link themselves together to form a pipeline without any supervising function responsible for calling them in a particular order. 

Probabilistic Programming in Python

Learn about probabilistic programming in this guest post by Osvaldo Martin, a researcher at The National Scientific and Technical Research Council of Argentina (CONICET) and author of Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ, 2nd Edition.

This post is based on an excerpt from the second chapter of the book that I have slightly adapted so it’s easier to read without having read the first chapter.

Data Manipulation with Pandas: A Brief Tutorial

Learn three data manipulation techniques with Pandas in this guest post by Harish Garg, a software developer and data analyst, and the author of Mastering Exploratory Analysis with pandas.

Modifying a Pandas DataFrame Using the inplace Parameter

In this section, you’ll learn how to modify a DataFrame using the inplace parameter. You’ll first read a real dataset into Pandas. You’ll then see how the inplace parameter impacts a method execution’s end result. You’ll also execute methods with and without the inplace parameter to demonstrate the effect of inplace.

data manipulation in python with pandas