Some people believe that data science is similar to computer science in different aspects. In this blog, I will explain in detail the overlapping fields and the particular differences between data science and computer science. This will also guide you to make a right carrer option of your interest.

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When speaking about its sub-branches, computer science has a subdomain such as analytics, artificial intelligence, web creation, programming, processing of natural languages, machine learning, and a few others, data science is primarily connected to data mining, deep learning, and big data.

Let’s take a look in a little more detail at the two domains, starting with their meanings. …


Regression and classification are many times confusing to many beginners in the field of Machine learning. Eventually, this will make it impossible for them to adopt the correct methodologies for solving problems with prediction.

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Regression and classification are both types of supervised machine learning algorithms, where a model is trained along with correctly labeled data according to the current model.

Before we dig deep into understanding the variations between algorithms for regression and classification. Let’s understand each algorithm first.

Regression Machine Learning Algorithm:

Regression algorithms estimate a continuous value based on the input variables. The primary objective of problems with regression is to approximate a mapping function based on the variables of input and output. If the target variable is a quantity such as income, ratings, height or weight, or a binary category likelihood then the regression model should be used. …


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When we are a beginner in the Machine Learning field, we often get confused with classification and regression analysis. Regression is applied to the problem when a real or continuous value needs to be predicted, such as “salary” or “prices of the houses”. In these problem statements the target value is continuous and can be classified into a “yes” or “no” category. In such cases, we need to apply regression techniques. In this blog, I will cover the basics of different regression techniques and it’s python implementation.

What is Regression?

Regression is a statistical approach that understands the possible relationship among variables. The study of regression clarifies the changes in parameters in relation to changes in the target predictors. To investigate or analyze the relationship between the dependent and independent set of variables, regression methods are applied. It covers the variety of data analysis techniques that are implemented in qualitative-exploratory research for analyzing infinite variables. The prime applications of regression analysis are for forecasting, time series analysis modeling, and defining cause-effect relationships. …


According to a survey and research, Data Science and Machine Learning are the most popular jobs posted in today’s era. There are many subfields and specialization in both domains. But, for a beginner sometimes these words sound like a vast ocean and for them, it might be difficult to sail the ship. Furthermore, the more you dig initially at different levels the more you feel confused about the tools. Basically, this blog is intended to put all the list of libraries that are necessary for one checklist. …


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Being a leader and working on different projects and with different members simultaneously, sometimes it is quite challenging to keep up the pace and manage the work efficiently. But thanks to digital platform and tools which are out there in the market to simplify our work and meet the goals.

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In this blog, I put forward different software tools and techniques for efficient project management with their features, uniqueness, their prices, and link to connect them. In the end, you will be able to figure out the best-suited one for your application.

1. PROOFHUB

ProofHub offers a replacement for conventional emailing, with many project management apps under one roof, and a lot of other tools. …


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When we are working on real-time scenarios in building the machine learning model, feature selection can be an important part of the process because it has the potential to significantly enhance the performance of our models. Training the model with all the data we have may lead to a common problem called the curse of dimensionality. In simple terms, this may be referred to as the phrase” Too many cooks will spoil the soup”.

What is feature selection?

Feature Selection is a mechanism in which you automatically pick the features in your data that most apply to the forecast attribute or performance you are interested in. …


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Future AI systems are ramped up into medical research and evolving the best practices in the healthcare sector. With constant improvement in a different paradigm, patient-centered service is a leading focus. This aims for improving between good health care and the healthcare that people actually receive. Using advanced human-generated data devices, multiple office visits for checkups, and routine treatment are replaced with remote monitoring. Providers can save time and increase the accuracy of their diagnoses when this tracking is combined with online consultations, driven by AI. …


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There is substantial demand in computational requirements for training neural networks in recent years. With the use of distributed training environments in which a neural network is split across multiple GPU and CPU devices. The challenge for distributed training is to find an optimized way to place multiple heterogeneous devices to achieve the fasts possible training speed.

This blog is intended to understand the basic concepts of device placement and different areas of research in this area.

What is device placement?

Let’s say we have 10 GPUs and have a graph with different TensorFlow operations like convolution, max pooling operations, and we need to optimally place these operations to 10 GPUs. Humans can do this task but determining an optimal device placement is very challenging. …


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Natural Language Processing (NLP) deals with text data. The applied research in NLP is motivated to design the technology that understands the human language more effectively. The research in NLP is more demanding and challenging as it is difficult to understand how the human brain understands the secrets of language and its communication methods.

Many laboratories, researchers from all around the world are working their best to synchronize between technology and human language with machine learning and deep learning frameworks. …


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In Analytics Vidhya, Hackathon, there was a problem statement for text prediction of topic/subject to which class it belongs basis on title and abstract. To solve this question of prediction problem I have applied Multinomial Naive Bayes classifier supervised algorithm.

In this blog, I have covered the importance of the Naive Bayes classifier, its types, and the actual implementation of the algorithm for the given problem statement.

What is Naive Bayes? and Why?

Naive Bayes Classifier Algorithm is a family of probabilistic algorithms based on applying Bayes’ theorem with the “naive” on the basis of two following assumption:

  1. Predictors are independent of each other.
  2. All features have an equal effect on the outcome. …

About

Dr. Monica

Research aspirant in Machine learning and Data Science

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