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Automating Machine Learning workflows with SkLearn-Pandas

Machine learning workflows include all the steps required to build machine learning models from raw data. These processes can be divided into the transformation and the training stages.

The transformation stage include the processes required to transform the raw data to features (feature engineering) while the training stage encapsulate the…


Human attributes identification and classification are popular aspects of computer vision which have been utilized in building relevant innovative systems in recent years. Most of these systems heavily rely on detection and recognition of facial attributes to perform efficiently. This project explores the use of an alternative approach to…

A Simplified and Detailed Explanation of Everything A Data Scientist Should know about Linear Regression Modelling

First and foremost, it is almost impossible to cover absolutely everything on this topic for various reasons. The aim of this blogpost is to simplify most of the concepts and show their practical applications as much as it concerns a data scientist.

So let’s go!


Linear Regression modelling is a…

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How to shift images along their axes using OpenCV and Imutils.

Translation in computer vision refers to the process of shifting an image along its axes. Images can be shifted in upwards, downwards, sideways directions or with a combination of these directions through the image translation process. Image translation is often useful in image data augmentation and image reconstruction. …

Mapping raw data to machine learning features

Feature engineering is one of the key steps in developing machine learning models. This involves any of the processes of selecting, aggregating, or extracting features from raw data with the aim of mapping the raw data to machine learning features.

Mapping raw data to feature vectors. (Image by author).

The type of feature engineering process that is applied to…

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How to draw lines, shapes and customize drawings on Images using OpenCV.

Lines, common and custom shapes can be drawn on images using OpenCV drawing functions. Coordinate points and spatial dimensions of images are used to designate positions and dimensions of drawings on images.

Drawing Basic lines

The cv2.line function is used to draw lines on images with OpenCV. It takes integer…

Understanding the Bias- Variance Trade-off in Machine Learning

The general goal of building supervised machine learning models is to develop a model to estimate a target (y) based on some features (x) by training on a set of data that correctly maps the relationship between the features and the target variables.

Photo by Umberto on Unsplash

Getting and setting image pixels for processing with OpenCV

Pixels are important attributes of images in computer vision. They are numerical values that represent the color intensity of light in a particular space in an image and are the smallest units of data in an image.

The total number of pixels in an image is obtained as the product…

How to to evaluate the performance of your model during training.

Photo by Markus Spiske on Unsplash

Validation is a technique in machine learning to evaluate the performance of models during learning. It is done by separating the data set into training and validating sets and then evaluating the performance of the model (deep neural network in this case) on the validation sets.

It is important to…

Samuel Ozechi

Data scientist, Machine Learning Engineer.

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