Brian Mutea

Software Engineer | Data Scientist with an appreciable passion for building models that fix problems and sharing knowledge.

TensorFlow Tensors(What are Tensors: Understanding the Basics, Creating, and Working with Tensors) Paid Members Public

First off: If you are familiar with NumPy arrays, understanding TensorFlow Tensors will be as easy as first importing TensorFlow as below: import tensorflow as tf print(tf.__version__) # check version # 2.14.0 💡The examples in this article use TensorFlow v2.x, so concepts deprecated and or that were

Entropy, information gain, and Gini impurity(Decision tree splitting criteria) Paid Members Public

Decision trees are supervised machine-learning models used to solve classification and regression problems. They help to make decisions by breaking down a problem with a bunch of if-else-then-like evaluations that result in a tree-like structure. For quality and viable decisions to be made, a decision tree builds itself by splitting

Decision Trees and Random Forests(Building and optimizing decision tree and random forest models) Paid Members Public

In the modern world, so much data is present on the internet. Organizations need efficient and rigorous algorithms to handle these huge chunks of data, make practical analyses, and provide appropriate decisions relevant to maximizing their profits and market presence. There are such algorithms commonly used today for decision-making processes.

Logistic regression in Python with Scikit-learn Paid Members Public

In linear regression, we tried to understand the relationship between one or more predictor variables and a continuous response variable. This article will explore logistic regression, where the response variable will be discrete or categorical. What is classification? Classification is a supervised machine learning problem of predicting which category or

Linear regression in Python with Scikit-learn (With examples, code, and notebook) Paid Members Public

Scikit-learn is a handy and robust library with efficient tools for machine learning. It provides a variety of supervised and unsupervised machine learning algorithms. The library is written in Python and is built on Numpy, Pandas, Matplotlib, and Scipy. In this tutorial, we will discuss linear regression with Scikit-learn. What

Seaborn tutorial Paid Members Public

Seaborn is a simple, easier-to-learn open-source data visualization Python library that provides fantastic default styles and color palettes to create attractive and informative statistical plots. Seaborn is built on top of Matplotlib. Matplotlib treats Figures and Axes as objects and focuses on how to draw them. Seaborn has a dataset-oriented,

Data visualization with Matplotlib Paid Members Public

💡"A good sketch is better than a long speech"(Napoleon Bonaparte).  Organizations collect and analyze vast amounts of data from sales revenue, marketing performance, customer interactions, inventory levels, production metrics, staffing levels, costs, etc. This can be too much data that it is impossible to effectively understand and

Python for data science tutorial (Complete guide with examples and notebook) Paid Members Public

This article will dive into fundamental Python concepts you need to understand before using Python for data science and machine learning. Let's dive right in! What is Python? Python is the language of preference for most data scientists. It is a general-purpose, high-level programming language that supports object-oriented,