Yellowbrick for creating machine learning plots with less code — Data visualization plays an important role in machine learning. Data visualization use cases in machine learning include: Hyperparameter tuning Model performance evaluation Validating model assumptions Finding outliers Selecting the most important features Identifying patterns and correlations between features Visualizations that are directly related to the above key things in machine…

With Selection, Slicing, Indexing and Filtering — In part 1 and part 2, we’ve learned how to inspect, describe and summarize a Pandas DataFrame. Today, we’ll learn how to extract a subset of a Pandas DataFrame. This is very useful because we often want to perform operations on subsets of our data. There are many different ways…

Reduce the size of your dataset while keeping as much of the variation as possible — In both Statistics and Machine Learning, the number of attributes, features or input variables of a dataset is referred to as its dimensionality. For example, let’s take a very simple dataset containing 2 attributes called Height and Weight. This is a 2-dimensional dataset and any observation of this dataset can…

Choose the Right Laptop for Data Science and Machine Learning — If you’re learning Data Science and Machine Learning, you definitely need a laptop. This is because you need to write and run your own code to get hands-on experience. When you also consider portability, the laptop is the best option instead of a desktop. A traditional laptop may not be…

K-Nearest Neighbors (KNN) Algorithm for Handling Missing Data — The K-Nearest Neighbors (hereafter, KNN) is a supervised machine-learning algorithm that uses a k number of nearest (closest) neighbors to classify an instance into a relevant class. Neighbors of an instance are found using the Euclidean distance. The Euclidean distance between two data points is calculated using the following formula.

Standardization, Normalization, Robust Scaling, Mean Normalization, Maximum Absolute Scaling and Vector Unit Length Scaling — Many machine-learning algorithms need to have features on the same scale. There are diffident types of feature scaling methods that we can choose in various scenarios. They have different (technical) names. The term Feature Scaling simply refers to any of those methods. Topics ------ 1. Feature scaling in different scenarios…

Data is all around us. Make them useful! — It would be almost impossible to utilize the state-of-the-art technology available all around us without machine learning and artificial intelligence. Data is the key ingredient of AI and ML algorithms. Massive amounts of data, low-cost computational resources and open-source software make ML and AI technology available for everyone. You have…

Utilizing Coarse-to-Fine Search (CFS) for an informed search of best neural network hyperparameters — Too many technical terms in the title and subtitle? Let me explain one by one! Hyperparameters are a type of variables that define how the model is trained. We need to manually specify the values of hyperparameters as they do not learn their values from the data. …

A step-by-step guide to cloning a GitHub package to your Windows computer — Anaconda is a popular Python and package distribution system for data science and scientific computing. Most commonly used packages come directly with Anaconda installation, so you don’t need to install them separately. We can manually install packages that don’t directly come with Anaconda distribution by using pipor condapackage installers. …