Step by step guide to build a Deep Neural Network that classifies Images of Dogs and Cats. Beginners Guide - CNN Image Classifier | Part 1

Learning how to build a basic logistic regression model in machine learning using python . Logistic regression is a commonly used model in various industries such as banking, healthcare because when compared to other classification models, the logistic regression model is easily interpreted.

So, due to my curiosity in astronomy, I decided to develop a Machine Learning project on Nasa’s exoplanet archive. By going into this project, I aimed to measure how efficiently works M.L algorithms to find these other planets.

Machine Learning in healthcare can be applied to digitally prognose (predictive diagnosis) using the risk factors of a disease. ML can detect patterns of certain diseases with patient electronic health records and report anomalies to the pattern. ML Diagnostic applications are increasingly being used to supplement clinicians’ decisions, using data lakes to condensify millions of observations of diseases. To instantiate the power of machine learning as a medical prognostic tool, I examined its use in cervical cancer to classify one’s risk of having it, using risk factors, using a publicly available Cervical Cancer Risk Classification Data Set. In this tutorial, you'll see Using Machine Learning to Prognose Cervical Cancer: A Step by Step Guide

Quora Question Pairs Similarity: Tackling a Real-Life NLP Problem. In this article, I will be walking you through the process of solving a real-life, NLP problem.

In this article, I’ll be discussing the way to achieve balanced datasets using various techniques, as well as compare them.

Will your employee leave? A machine learning model. Data-driven study of 14,249 past and present employees.

Active learning is a sub-field of Artificial Intelligence which is based on the fact that curious algorithms are better learners both in terms of efficiency and expressivity.

A step-by-step guide on how to calibrate your Machine Learning Classifiers. How to enforce the outcome of your Machine Learning Classifiers

Why Is Logistic Regression the Spokesperson of Binomial Regression Models? A small discussion on the binomial regression model and its link functions

In this post, I will go over how I created a function in Python that easily displays some performance metrics of a trained classification model. Since this function came about as part of a larger task, I will provide context along the way to help clarify as I share images and code of my process.

Machine Learning algorithms tend to produce unsatisfactory classifiers when faced with imbalanced datasets. Different methods to handle imbalanced data when solving classification tasks

Text Classification Using Naive Bayes: Theory & A Working Example. In this article, I explain how the Naive Bayes works and I implement a multi-class text classification problem step-by-step in Python.

Here you will find: Data Cleaning, Feature Selection, Bayesian Optimization, Classification, and Model Validation. We want to minimize the rate of FP and FN as well as maximize the rate of TP. To do so, we will use the metric AUC (area under the curve) of ROC Curve (receiver operating characteristic), because it returns us the best model as well as the best threshold.

Pycaret is an open-source machine learning library in python to train and deploy supervised and unsupervised machine learning models in a low-code environment.

Conceptually with multiple examples. In this post, we are going to discuss the workings of Naive Bayes classifier conceptually so that it can later be applied to a real world dataset.

A case study with KNN, Logistic Regression, Gaussian NB, Decision Trees and Random Forest. In this blog, let me take you through the model that I have built based on a flight survey data and demonstrate the business value that it can potentially create.

conceptually with example using ID3 algorithm. In this post, we are going to discuss the workings of Decision Tree classifier conceptually so that it can later be applied to a real world dataset.

What is Naive Bayes algorithm? It is a classification technique based on Bayes' Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Interactive Visualization of the Outcome of Any Binary Classifier — in 5 Lines of Python. Make an impactful plot of your model outcome with “confusion_viz”