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…

By making the feature importances plot — Do you want to build a random forest model using the most important feature in the dataset? If “Yes”, how do you select the most important features in random forest? Today, I’ll answer this question by building a random forest model with the most important features in the dataset. Not…

Cleaning corrupted images using convolutional and feedforward autoencoders — Do you want to find out how convolutional autoencoders outperform feedforward autoencoders in image denoising? If ‘Yes’, just keep reading this article. Different types of autoencoders There are many practical applications of autoencoders. Image denoising is one of them. Image denoising refers to removing noise from corrupted images to get clean images.

LDA for dimensionality reduction with and without feature scaling — Linear discriminant analysis (hereafter, LDA) can be used for linear dimensionality reduction. In most cases, it is necessary to do feature scaling before Principal Components Analysis — PCA, which is another linear dimensionality reduction technique. Does the same apply for LDA? This is a question that most beginners have in…

Best laptops for data science, machine learning and deep learning under $800. No MacBook included! See the reasons at the end — There are many necessary requirements for a perfect laptop for data science, ML and DL tasks: CPU performance GPU availability RAM size Reliability and durability Eye care display Portability Battery life It is hard to find a laptop that meets all these requirements! But somehow, I managed to find 4…

Running the PCA algorithm twice is the most effective way of performing PCA — What is the dimensionality of a dataset? In the context of both statistics and machine learning, the dimensionality of a dataset refers to the number of input variables (features) in the dataset. If the dataset contains only two input variables as in the following image, it is called a two-dimensional dataset. In this case, the observations (data…