My goal in writing this article is to:
A little more than a year ago, our world has changed. COVID-19 pandemic forced the majority of us to go for social isolation.
As a Data Scientist, I look at it in the following way.
Even while staying indoors, we can collect and analyze some data (directly from our web camera, for example), right?
Processing and analysis require us either write our own image-processing libraries or using already developed ones.
Previously, I wrote about analyzing COVID-19 scientific papers…
The model evaluation shows us how the model performs in the real world.
This topic might be confusing to many people. If you have found yourself overwhelmed by the amount of information, just take it easy, slow down, and come back to basics.
I will try to explain what I understand about the Performance Evaluations of Machine Learning (ML) models in simple words and a few animations.
There are two kinds of predictive modeling, depending on the kind of data we are dealing with:
Let us talk about Artificial Neural Networks (NNs): what are these beasts and how do they work under the hood. In the next Part, we will build our own NN from scratch in Python to get a real feeling of how it all works.
I intend to explain it in simple words, so everybody can understand what is going on here.
So, let us begin.
An Artificial Neural Network (in short just NN) is a computing system that tries to mimic a human brain.
Here is my little animation to visualize the NN structure:
This article is part of my big project. The main idea is to teach everybody Python, Data Science, and Machine Learning (ML) despite their educational background. I try to explain everything in simple terms but not compromise the quality.
If ML algorithms were a tasty soup, it is still possible to eat it without knowing what is inside. However, I prefer to shed light on the ingredients and the way it was cooked.
We already covered the reasons for learning Machine Learning, went through general terms/definitions, explored which common Python libraries are used for Data Science and ML, and loaded…
The Support Vector Machine algorithm (or SVM) is a classification algorithm that classifies cases by separating them one from another.
The point of SVM is to separate the data into classes by finding a boundary/separator.
In SVM, data points that are on the one side from that separator belong to one class, and those points on the other side belong to another class.
One might wonder: if we want to classify unknown cases why do not use a classification algorithm that works by classifying the data points directly (such as K-Nearest Neighbors) instead of classifying them by separation?
A step-by-step Exploratory Data Analysis project: from Business problem description to solution and implementation (with code). Using Foursquare API, Beautiful soup, Requests, Pandas, and Folium.
I created this project in order to complete a Capstone Project to obtain IBM Professional Data Science Certificate. I came up with this business idea because it resonates with me as a promoter of a healthy lifestyle. So I would like to share it with you step-by-step.
The article consists of the following chapters:
The K-Nearest Neighbors algorithm (or K-NN) is a classification algorithm that takes a batch of labeled points and uses them to learn how to label other points.
K-NN algorithm classifies cases based on their similarity to other cases.
In K-Nearest Neighbors, data points that are close to each other are said to be neighbors.
Similar cases with the same class labels are close to each other in the feature space. Thus, the distance between the two cases is a measure of their similarity or conversely, their dissimilarity.
Essentially, it comes down to calculating the distance between two data points.
Hi there! A while ago, I have been talking to a friend and the topic touched on Machine Learning. And by the end of the conversation, I concluded that many people have some prejudice against the whole topic of “intellectual machines”, or even might be afraid to start the learning process due to the overwhelming amount of information. So,
I decided to break the ice for everyone who is just starting out on this journey of mastering the Machine Learning.
Let us be clear — Machine Learning is not Magic. Machine Learning (so-called ML) is the study of computer algorithms…
This tutorial is meant for everyone interested in Python. Especially, for those who are just starting out and cannot break the ice from intention to action. You are here which means you are serious about learning Python and I appreciate it.
I am using Python for my work a lot. Some of the applications are scientific computing, statistics, and advanced visualization. But in my free time, I enjoy creating mini-applications for various reasons. Some of them solve a particular problem (personal, or a world large-scale problem), others are just for fun, or just something to challenge myself.
This particular lesson…
After the short introduction to clustering and the practical ideas of using it, we will go through this tutorial on K-Means Clustering from scratch in Python. I will show you how it works intuitively step by step, in a way I wish somebody showed it to me. After completing this tutorial, you will learn how to:
numpy, a basic Python library,
Imagine that we are a company that…