Learn how loss functions are used to understand your model’s performance and their implementation in regression and classification problems hands-on in Python.

In this article, we will be diving deep into the different types of loss functions used for improving a machine learning algorithm’s performance. The main motive of any classical machine learning problem is just these two things:

  1. Improve the accuracy of the model and
  2. Decrease the loss associated with that.

Easy step-by-step Installation and Usage of HDFS on your system using the Docker image.

In this small article, we would be discussing how to set up the Docker-based Hadoop distributed file system on your computer and will discuss a simple example to demonstrate the use-case. …

Interpreting measures of error for time series data and understand the performance of your model to choose the best one

In this article, we are going to talk about the types of error measuring techniques when dealing with the time-series data and how you can choose the best model from the ones you have created earlier such as ETS or the ARIMA model, and find the best ones based on…

Making Sense of Big Data, Data Science, Machine Learning

A simple solution for data analytics for big data parallelizing computation in Numpy, Pandas, and Scikit-Learn Frameworks


If you are dealing with a large amount of data and you are worried that Pandas’ data frame is unable to load it or NumPy arrays get stuck in between and you even need a much better and parallelized solution for your data processing and training machine learning models then…

Understanding the Science behind a Bell Curve!

A Normal Distribution is also called “Gaussian Distribution” or more commonly known as “Bell Curve” as the probability distribution function plot of a normal distribution looks very like 🔔 bell-shaped.

A Normal Distribution is a univariate probability distribution, which means it is a distribution for only one random variable. Note

A Practical Approach to Compute the Model’s Performance and Implementation in Python covering all Mathematical Reasonings. Explained!

Welcome! Are you ready with your cool machine learning model trained on millions of data points and now you want to test its performance but you don’t know where to start with or what could be a better way to do so?

In this article, we would be discussing everything…

“Understanding the concept of Q-Q plots”.

In Statistics, Q-Q(quantile-quantile) plots play a very vital role to graphically analyze and compare two probability distributions by plotting their quantiles against each other. …

How to Make a Strong Portfolio that Speaks about you in your interviews!

There are people who are working hard day and night and still looking for a job and then landing with nothing in hand! This has been one of the most common questions by job applicants looking for a career in the field of Data Science and Machine Learning.

How to…

I built my complete Blog website using this approach and it turns out to be really cool and all that for free. Learn how you can even do it!

I just made my own website for free and hosted it on WordPress for Free with my custom domain name and all the premium features. Want to know how I did it, follow along with me in this article and know how you can also make your own!

Hey, If…

The complete compilation of my checklist to learn Data Science for a Beginner to a master is just one year with time travel storytelling. Enjoy Learning!

Finally, this article is something that we all have been waiting for. In this complete article, we would be discussing how can a complete freshman can start their journey in the vast field of Machine Learning and Data Science starting from learning core concepts and writing basic codes all the…

Paras Varshney

Data Scientist at IUDX, IISc Banglore • Kaggle Master • Explorer • Writer • https://parasvarshney.ml

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