Author Archives: Ajitesh Kumar

Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin and Twitter.

Elbow Method vs Silhouette Score – Which is Better?

In K-means clustering, elbow method and silhouette analysis or score techniques are used to find the number of clusters in a dataset. The elbow method is used to find the “elbow” point, where adding additional data samples does not change cluster membership much. Silhouette score determines whether there are large gaps between each sample and all other samples within the same cluster or across different clusters. In this post, you will learn about these two different methods to use for finding optimal number of clusters in K-means clustering. Selecting optimal number of clusters is key to applying clustering algorithm to the dataset. As a data scientist, knowing these two techniques to find …

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Posted in Data Science, Machine Learning, Python. Tagged with , , , .

Hold-out Method for Training Machine Learning Models

Hold-out-method-Training-Validation-Test-Dataset

The hold-out method for training the machine learning models is a technique that involves splitting the data into different sets: one set for training, and other sets for validating and testing. The hold out method is used to check how well a machine learning model will perform on the new data.  In this post, you will learn about the hold out method used during the process of training machine learning model. Do check out my post on what is machine learning? concepts & examples for detailed understanding on different aspects related to basics of machine learning. When evaluating machine learning (ML) models, the question that arises is whether the model …

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Posted in Data Science, Machine Learning. Tagged with , .

Hello World – Altair Python Install in Jupyter Notebook

Altair visualization python

This blog post will walk you through the steps needed to install Altair graphical libraries in Jupyter Notebook. For data scientists, Altair visualization library can prove to very useful. In this blog, we’ll look at how to download and install Altair, as well as some examples of using Altair capabilities for data visualization. What is Altair? Altair is a free statistical visualization library that can be used with python (2 or 3). It provides high-quality interactive graphics via an integrated plotting function ́plot() that produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Altair is also easy to learn, with intuitive commands like ‘plot’, ‘hist’ …

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Posted in Data Science, Python. Tagged with , .

Most In-Demand Skills for Data Scientists in 2022

most in-demand data science skills

The data science field is growing rapidly and data scientists are in high demand. If you want to enter this field, it’s important that you have the right skills. In this blog post, we’ll explore the most in-demand skills of data scientist employers are looking for the most and how to develop these skills so that you can find a job as a data scientist. Strong experience with statistical/ML methods: Strong familiarity with probability distributions (e.g., normal distribution), concepts of hypothesis testing, regression analysis is essential for becoming a great data scientist. Data scientists are required to model data, assess the suitability of data for analysis, develop mathematical / machine …

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Posted in Career Planning, Data Science, Interview questions. Tagged with , .

Different types of Machine Learning: Models / Algorithms

supervised vs unsupervised machine learning

Machine learning is a type of machine intelligence that enables computers to learn and improve without being explicitly programmed. It’s based on the idea that we can build systems which allow our data to do the talking, by finding patterns in vast quantities of information. These machine learning algorithms require different types of machine-learning models trained using different algorithms, depending on what problem they are trying to solve or how accurate an answer needs to be. In this blog post, we will discuss the following four different types of machine learning models / algorithms: Supervised learning Unsupervised learning Semi-supervised learning Reinforcement learning What is supervised learning? Supervised learning is defined …

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Deep Neural Network Examples from Real-life

deep neural network examples from real-life

The deep neural network (DNN) is an artificial neural network, which has a number of hidden layers and nodes. Deep NN is composed of many interconnected and non-linear processing units that work in parallel to process information more quickly than the traditional neural networks. Deep learning algorithms are used for classification, regression analysis, prediction and other types of tasks. In this blog post, we will present deep neural network examples from the real-world/real-life. Before jumping into examples, you may want to check out some of my following posts on deep neural network: Deep Learning Explained Simply in Layman Terms Neural network explained with perceptron example Perceptron explained with Python example …

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Posted in Deep Learning, Machine Learning. Tagged with , .

Difference between Supervised & Unsupervised Learning

Supervised vs Unsupervised Machine Learning Problems

Supervised and unsupervised learning are two different common types of machine learning tasks that are used to solve many different types of business problems. Supervised learning uses training data with labels to create supervised models, which can be used to predict outcomes for future datasets. Unsupervised learning is a type of machine learning task where the training data is not labeled or categorized in any way. For beginner data scientists, it is very important to get a good understanding of the difference between supervised and unsupervised learning. In this post, we will discuss how supervised and unsupervised algorithms work and what is difference between them. You may want to check …

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Posted in AI, Data Science, Machine Learning. Tagged with , .

Data Governance Framework Template / Example

data governance framework template

Data governance is a framework that provides data management governance. It’s the process of structuring data so it can be governed, managed and used more effectively. Data governance framework forms the key aspect of data analytics strategy. This blog post will discuss key functions of a standard data governance framework and can be taken as a template or example to help you get started with setting up your data governance program. What is Data Governance Framework? The data governance framework is intended to put some structure around how data can be managed and used in an organization based on well-defined rules and processes around a variety of data related operations and decisions. Data …

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Posted in Data, Data analytics. Tagged with , .

Business Analytics Team Structure: Roles/ Responsibilities

business analytics team structure roles and responsibilities

Business analytics is a business function that has been around for years, but it’s only recently gained traction as one of the most important business functions. Organizations are now realizing how business analytics can help them increase revenue and improve business operations. But before you bring on a business analytics team, you need to determine if your company needs a full-time or part-time team member or both. It might seem logical to hire full-time analysts just because they’re in demand, but this isn’t always necessary. If your business operates without any external data sets and doesn’t have complex reporting needs then it may be more cost-effective to use freelancers rather …

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Posted in Data analytics, Data Science, Machine Learning, Product Management. Tagged with , , .

Google Cloud Automl: Business Application Examples

Google cloud platform GCP Automl Services

Google cloud platform (GCP) automl services are a set of google cloud platform products with a focus on machine learning and automation. They help you to automate several tasks related to machine learning. In this blog post, we’ll talk about google cloud automl services and some common business problems that can be solved using these GCP automl services. What are some popular Google Cloud Automl services? Google cloud automl services include some of the following: Google Cloud Vision can be used to perform tasks related to image recognition like face detection, OCR (optical character recognition), landmark detection, etc. Google’s cloud vision can detect emotions, understand text, and more. The service …

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Posted in Google Cloud, Machine Learning. Tagged with , , .

Autoregressive (AR) models with Python examples

Autoregressive (AR) models are a subset of time series models, which can be used to predict future values based on previous observations. AR models use regression techniques and rely on autocorrelation in order to make accurate predictions. This blog post will provide Python code examples that demonstrate how you can implement an AR model for your own predictive analytics project. You will learn about the concepts of autoregressive (AR) models with the help of Python code examples. If you are starting on time-series forecasting, this would be useful read. Note that time-series forecasting is one of the important areas of data science / machine learning. Here are some of the topics that will be covered …

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Posted in Data Science, Machine Learning, Python. Tagged with , , .

Neural Network Explained with Perceptron Example

Single layer neural network

Neural networks are an important part of machine learning, and so it is essential to understand how they work. A neural network is a computer system that has artificial neurons. It can be built to solve tasks, like classification and prediction problems. The perceptron algorithm is an example of how neural networks work. They were first proposed by Frank Rosenblatt in 1957 as models for the human brain’s perception mechanism. This post will explain the basics of neural networks with a perceptron example. You will understand about how a neural network is built using a perceptron. This is a very important concept in relation to getting a good understanding of …

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Posted in Data Science, Deep Learning, Machine Learning. Tagged with , .

What is Explainable AI? Concepts & Examples

Explainable AI - SHAP Explainability

Explainable AI is an important part of artificial intelligence. It provides humans with the ability to explain how decisions are made by machines. This helps people trust and understand what’s happening, instead of feeling like their information is being taken advantage of or used without their permission. In this blog post, we’ll explain explainable AI and explain it with examples so you know how it works! What is Explainable AI and how does it work? Explainable AI is defined as AI systems that explain their reasoning behind the prediction. Explainable AI is part of the larger umbrella term for artificial intelligence known as “interpretability.” Explainable AI is a type of …

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NIT Warangal offers one-week online training on AI, Machine Learning

NIT Warangal one week course on AI and machine learning

Are you interested in learning about AI and Machine Learning, or refresing your concepts? NIT Warangal offers one-week online paid training (minimal fees) on AI, Machine Learning. This program is a great opportunity for students to learn about AI & machine learning basics and advanced concepts. It is organized by the Department of Electronics and Communication Engineering & Department of R&D in association with Center of Continuing Education. It will be taught by experience professors who have years of experience in their respective fields. The course will take place between 30th November to 4th December 2021, and it is open to all Faculty/ Research Scholars/Industry professionals/ and other eligible students …

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Posted in AI, Data Science, Machine Learning, Online Courses. Tagged with , , .

What is Machine Learning? Concepts & Examples

what is machine learning

Machine learning is a machine’s ability to learn from data. It has been around for decades, but machine learning is now being applied in nearly every industry and job function. In this blog post, we’ll cover what machine learning entails, how it differs from traditional programming. What is machine learning? Simply speaking, machine learning is a technology where in machine learns to perform a prediction/estimation task based on past experience represented by historical data set.  There are three key aspects of machine learning which are following: Task: Task can be related to prediction problems  Experience: Experience represents historical dataset Performance: The goal is to perform better in the prediction task …

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Posted in Data Science, Deep Learning, Machine Learning. Tagged with , , .

ML Engineer vs Data Scientist: Differences & Similarities

ML Engineer vs Data Scientist

In today’s world, ML (machine learning) engineer and Data scientist are two popular job positions. These positions have a lot of overlap but there are also some key differences to be aware of. In this blog post, we will go over the details of ML engineers vs Data scientists so you can decide which one is right for you! What does an ML engineer do? An ML engineer primarily designs and develops machine learning systems. Before getting into the roles & responsibilities of an ML engineer, let’s understand what is a machine learning system. A machine learning system can be defined as a system that comprises of one or more …

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Posted in Data Science, Machine Learning. Tagged with , .