In this module, you’ll learn what is a loss function, how a machine learning model iteratively reduces loss, and how you can use the ‘learning rate’ hyperparameter to optimize the loss function.

**Loss** is a number that indicates the difference between the model predictions to the actual values (labels). When…

Machine learning models are increasingly used to make decisions or to inform decisions. For e.g. A model might influence a decision for approval of a loan, screening candidate resumes for a job application, etc. Such decisions are crucial and we need to be confident that our models don’t discriminate against…

I‘ve had almost 8 years of professional experience now and 5 years specifically in the field of data science and machine learning. In my current role, my team and I design and build predictive machine learning models and promote emerging technologies within the department. …

Maths and statistics are powerful tools in the world of data science. Math and Statistics are essential because these two fields form the basics of all the machine learning algorithms. And in order to succeed as a Data Scientist, you must know your basics.

Statistics is the use of maths…

An activation function is an internal state of a neuron that converts an input signal to an output signal.

Basically, a neuron calculates the weighted sum of its inputs, adds the bias, and then inputs the values to the activation function which decides whether it should spit an output or…

`Polynomial Regression | Data Science | Machine Learning`

In this article we will learn what is Bayesian Information Criterion (BIC) and how it is used to choose the degree of a polynomial in a Polynomial Regression.

Sometimes R2 values vary slightly across two different degrees of polynomials. i.e. comparing a R2 score = 88.3% to R2 score =…

If you fail to plan, you plan to fail. Every project requires planning. Building a machine learning model is no different. In this article, we will learn how to plan your data mining activities and what are the steps you should perform during Exploratory Data Analysis (EDA). This article is…

**Simple linear regression suffers from two major flaws:**

- It’s prone to overfitting with many input features and,
- It cannot easily express non-linear/curvy relationships.

One way to tackle these issues is by increasing the model complexity. …

Advances in smart assistants like Alexa and Google have brought remarkable convenience into our day to day lives. e.g. seeking a quick weather report, translating languages, listening to world news, and today you can also send virtual hugs to your Alexa contacts. …

In this article we will find answers to the following questions:

- What is a Z-score — Formula and definition.
- How to use Z-score using a toy example.

History: The letter **‘Z’ **in z-score stands for **Zeta** (6th letter of the Greek alphabet) which comes from the Zeta Model that was…

Data Scientist and Project Management Professional at Government of Canada. Visit https://swapnilklkar.github.io for more.