That’s it.

It is the simplest form of Regression Analysis

Example

There is a group of people that I measured, and the 2 features that I measured of these people are their weight and their high level.

Making it clear the weight on the x-axis and the high level on the y-axis.

Plotting the points of people’s Weight against their heights. And I can see that there is a Linear relationship.


React is JavaScript Library which is declarative in nature and works great with dynamic data(unlike HTML). The React library is used for User Interfaces.

As defined earlier, React is a JavaScript library and not a Framework which raises a question:

React being a library and not a framework is good. But Why?

Here's why:

  • Frameworks are not flexible as they require you to follow rules.
  • Frameworks are full of features and difficult to customize.

Prerequisites for React:

  1. Basics of Javascript(Variables, Objects, Functions, Loops)
  2. Learning JS(https://jscomplete.com/learn)

So why should you use React?

  • The ability to work with a friendlier and optimized VIrtual Browser (Virtual-DOM) as compared to Real DOM.
  • You…


Lets dive right into ECMA Script

ECMA(European Computer Manufacturers Association) Script is an official specification that is generic in nature. JavaScript conforms to ECMA Script specifications which makes JavaScript interoperable across web browsers.

ECMA Script technical committee, known as TC39, makes yearly releases of ECMA Script and modern browsers implement the new features each year. ES6 is the version of ECMA Script 2015.

JavaScript Variables and Block Scopes

Prior to ES2015, JavaScript supported only function level scoping unlike other languages like C++/Java which has block level scoping. With ES2015, in addition to function level scoping, JavaScript also supports block level scoping.

Block scope is created by pair of curly braces i.e.{}…


It can be chaotic for the HR team to find the right candidate to hire for an open job position because organizations receive hundreds, thousands, or even hundreds of thousands of candidates that are available in the firm’s a resource/resume database.

It becomes almost impossible and inefficient to go through all the applications, resumes, and CV one by one.

So what if there were a way, to actually take historical data on who actually got hired, and map that to things that are found on their resume. …


Very common technique in machine learning, where you just try to take a bunch of data and find interesting clusters of things, just based on the attributes of the data itself.

Sounds fancy, but it’s actually pretty simple. All we do in K-means clustering is try to split our data into K groups, that’s where the K comes from: it’s how many different groups you’re trying to split your data into.

An unsupervised learning technique where you have a collection of stuff that you want to group together into various clusters. …


Did you ever wonder how the spam classifier in your email works? How does it know that the email might be spam or not?

One popular technique is something called, Naive Bayes, and that’s an example of a Bayesian method, so let’s learn more about how that works.

Bayes Theorem


Introduction to ML

So what is machine learning? Well if you look it up on the internet it’ll say that it’s algorithms that can learn from observational data and can make predictions based on it.

But in reality, these techniques are usually very simple: we take a set of observational data, we fit a line to it, and then we can use that line to make predictions.


Recently I was training a machine learning model and found something which I have never experienced before. The model I trained worked well in the validation and testing phase.

As I deployed it and tested it against real-life cases, its accuracy just dropped drastically. I searched, and that's when I learned about Data Leakages.

Data Leakage is the use of information in the model training process which would not be available at prediction time, causing the predictive scores (metrics) to overestimate the model’s utility when running in a production environment.

Let me explain the complex definition, we usually split datasets…


Have you ever been in a situation where you don't know whether to use Analysis or Analytics?

Which word to use?

The similarity of the words: Analysis and Analytics lead people to believe they share the same meaning and use them interchangeably.

Technically, this isn’t correct. There is in fact a distinction between the two.

So let’s clear this up.

First, we will start with Analysis.

Analysis

Consider you have a vast data set containing data of various types.


We will build a simple form of Object Recognition System. Although the example we’ll use is very simple, it reflects many of the same key machine learning concepts that go into building real-world commercial systems.

The dataset we will use is a small, very simple, for training a classifier to distinguish between distinct types of fruit.

To create the original dataset, we go to a nearby store, bought a few dozen oranges, lemons, and apples of different varieties, and recorded their measurements in a table. We notice the height and the width, estimated their mass.

We’ve formatted data slightly and…

Zain Ul Ebad

Computer Scientist | Content Writer | Intrapreneur

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