In this tutorial, you’ll learn how to use the OneHotEncoder class in Scikit-Learn to one hot encode your categorical data in sklearn. One-hot encoding is a process by which categorical data (such as nominal data) are converted into numerical features of a dataset. This is often a required preprocessing step since machine learning models require […]
One-Hot Encoding in Scikit-Learn with OneHotEncoder Read More »
In this tutorial, you’ll learn how to calculate the mean absolute error, or MAE, in Python. The mean absolute error can help measure the accuracy of a given machine learning model. The MAE can be a good complement or alternative to the mean squared error (MSE). By the end of this tutorial, you’ll have learned: What
How to Calculate Mean Absolute Error (MAE) in Python Read More »
In this tutorial, you’ll learn how to use the Pandas to_datetime function to convert a Pandas column to date time. Pandas provides a huge number of methods and functions that make working with dates incredibly versatile. However, data aren’t always read correctly. By the end of this tutorial, you’ll have learned: How to use the
Pandas to_datetime: Convert a Pandas String Column to Date Time Read More »
In this tutorial, you’ll learn how to use Python to create a switch-case statement. Prior to Python version 3.10, Python did not have an official switch-case statement. In order to accomplish this, you had a number of different options, such as if-else statements and dictionaries. By the end of this tutorial, you’ll have learned: How
Python Switch (Match-Case) Statements: Complete Guide Read More »
In this tutorial, you’ll learn how to use Python decorators on a function. Python decorators are useful tools to help you modify the behavior of a function. Similarly, decorators allow you to embrace the Don’t Repeat Yourself (DRY) programming principles for common operations you want to apply to multiple functions. By the end of this tutorial,
Python Decorators: How and Why to Use Python Decorators Read More »
In this tutorial, you’ll learn how all you need to know about the K-Nearest Neighbor algorithm and how it works using Scikit-Learn in Python. The K-Nearest Neighbor algorithm in this tutorial will focus on classification problems, though many of the principles will work for regression as well. The tutorial assumes no prior knowledge of the
K-Nearest Neighbor (KNN) Algorithm in Python Read More »
In this tutorial, you’ll learn how to use Python to calculate the MAPE, or the mean absolute percentage error. This error is often used to measure the accuracy of machine learning models. By the end of this tutorial, you’ll have learned: What the Mean Absolute Percentage Error is What a good value for the MAPE
How to Calculate MAPE in Python Read More »
In this tutorial, you’ll learn how to use GridSearchCV for hyper-parameter tuning in machine learning. In machine learning, you train models on a dataset and select the best performing model. One of the tools available to you in your search for the best model is Scikit-Learn’s GridSearchCV class. By the end of this tutorial, you’ll
Hyper-parameter Tuning with GridSearchCV in Sklearn Read More »
In this tutorial, you’ll learn how to use the Numpy random.normal function to create normal (or Gaussian) distributions. The functions provides you with tools that allow you create distributions with specific means and standard distributions. Additionally, you can create distributions of different sizes. By the end of this tutorial, you’ll have learned: Let’s get started!
Numpy Normal (Gaussian) Distribution (Numpy Random Normal) Read More »
In this tutorial, you’ll learn how to use Python to calculate the Manhattan distance. The Manhattan distance is often referred to as the city block distance or the taxi cab distance. The Manhattan distance can be a helpful measure when working with high dimensional datasets. By the end of this tutorial, you’ll have learned: What
Calculate Manhattan Distance in Python (City Block Distance) Read More »
In this tutorial, you’ll learn how to calculate the hamming distance in Python, using step-by-step examples. In machine learning, the Hamming distance represents the sum of corresponding elements that differ between vectors. By the end of this tutorial, you’ll have learned: Common applications of the Hamming Distance in machine learning, How to calculate the Hamming
Calculate Hamming Distance in Python (with Examples) Read More »
In this tutorial, you’ll learn how to use Python to prepend a list. While Python has a method to append values to the end of a list, there is no prepend method. By the end of this tutorial, you’ll have learned how to use the .insert() method and how to concatenate two lists to prepend.
Prepend to a Python a List (Append at beginning) Read More »
