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.

Gaussian Mixture Models: What are they & when to use?

gaussian mixture models 1

Gaussian mixture models (GMMs) are a type of machine learning algorithm. They are used to classify data into different categories based on the probability distribution. Gaussian mixture models can be used in many different areas, including finance, marketing and so much more! In this blog, an introduction to gaussian mixture models is provided along with real-world examples, what they do and when GMMs should be used. What are Gaussian mixture models (GMM)? The Gaussian mixture model is defined as a clustering algorithm that is used to discover the underlying groups of data. It can be understood as a probabilistic model where Gaussian distributions are assumed for each group and they …

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

Credit Card Fraud Detection & Machine Learning

credit card fraud detection machine learning

Credit card fraud detection is a major concern for credit card companies. With credit cards so prevalent in our society, credit card companies must be able to prevent credit card fraud and protect their customers. Machine learning techniques can provide a powerful and effective way of detecting credit card fraud. In this blog post we will discuss machine learning techniques that data scientists can use to design appropriate credit card fraud detection solutions including algorithms such as Bayesian networks, support vector machines, neural networks and decision trees. What are different types of credit card fraud? The following are different types of credit card fraud: Counterfeit credit cards: Counterfeit credit cards …

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

BigQuery ML Concepts & Examples: Starter Guide

google cloud bigquery ml

BigQuery ML is a machine learning platform that allows data scientists to build models using the power of their data. Unlike traditional machine learning, BigQuery ML does not require any programming skills, making it an easy way to get started with machine learning. Product managers and data scientists can both benefit from BigQuery ML by finding insights in their own datasets or collaborating with one another on new applications. The introduction of BigQuery Machine Learning Platform has enabled organizations to take advantage of the benefits of machine learning without needing deep expertise in either big-data or analytics technologies. This blog post will provide an overview of what you need to …

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

Quantum machine learning: Concepts and Examples

quantum machine learning hello world concepts and examples

Machine learning has been a hot topic for many years now. There are different types of machine learning algorithms that data scientists and engineers use in their projects, depending on the type of problem they’re trying to solve. Recently, quantum machine learning has emerged as an alternative to classical machine learning techniques. The future of quantum computing holds tremendous possibilities promising exponential speedups over current technology. In this blog post, we’ll explore quantum machine learning (QML), its benefits over traditional machine learning methods, and the common quantum computing concepts it relies on. What are key concepts related to quantum computing? Quantum computing takes advantage of the computing power available through …

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

Supplier Relationship Management & Machine Learning

supplier relationship management machine learning

Supplier relationship management (SRM) is the process of managing supplier relationships to develop and maintain a strategic procurement partnership. SRM includes focus areas such as supplier selection, procurement strategy development, procurement negotiation, and performance measurement and improvement. SRM has been around for over 20 years but we are now seeing new technologies such as machine learning come into play. What exactly does advanced analytics such as artificial intelligence (AI) / machine learning (ML) have to do with SRM? And how will AI/ML technologies transform procurement? What are some real-world machine learning use cases related to supplier relationships management? What are a few SRM KPIs/metrics which can be tracked by leveraging …

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Posted in Artificial Intelligence, Data Science, Machine Learning, Procurement. Tagged with , , .

Relationship: Analytics & Data-Driven Decision Making

analytics and data-driven decision making relationship

Data analytics is a topic that many data-driven organizations are becoming increasingly interested in. Data analytics often includes the process of analyzing data to find insights that can be used to make decisions. But what does this mean? How are different types of analytics related to data-driven decision-making? This blog post will explore how an organization’s use of data can help them make better, more informed decisions. Before getting into the details, lets quickly understand how business analytics is related data analytics. There are a number of facets that business analytics and data analytics have in common. In both the cases, the common steps include dealing with gathering data from …

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Posted in Data analytics. 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 , , .

NLP Pre-trained Models Explained with Examples

NLP pretrained models

The NLP (Natural Language Processing) is a branch of AI with the goal to make machines capable of understanding and producing human language. NLP has been around for decades, but it has recently seen an explosion in popularity due to pre-trained models (PTMs) which can be implemented with minimal effort and time on the side of NLP developers. This blog post will introduce you to different types of pre-trained machine learning models for NLP and discuss their usage in real-world examples. Before we get into looking at different types of pre-trained models for NLP, let’s understand the concepts related to pre-trained models for NLP. What are pre-trained models for NLP? …

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Posted in Deep Learning, NLP. Tagged with , .

Data-Driven Decision Making: What, Why & How

data driven decision making what why how

Data-driven decision-making is a data-driven approach to making decisions. This data can come from data analysis, data visualization, or other data resources. Data-driven decision-makers use data in their decision process and they make decisions based on the data that they have collected. The goal of this type of decision-maker is to make informed decisions rather than quick ones. In this blog post, we will discuss what data-driven decision-making is, how it differs from other types of decision-making, and why you should consider going for this method in your business! What are the different types of decisions? The following represents different types of decisions made in an organization on a day-to-day …

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Covid-19 Machine Learning Use Cases

covid19 machine learning use cases

The covid-19 virus is a type of coronavirus. It has been linked to severe acute respiratory syndrome (SARS). The covid-19 virus can be contracted through contact with saliva or mucous from an infected person. Symptoms include fever, cough, sore throat, headache, muscle aches, and fatigue. There are several problems related to the Covid-19 pandemic which can be solved using machine learning/data science techniques. In this blog post, we will look into some of these Covid-19 use cases which can be solved using machine learning classification and clustering techniques. What are Covid-19 data sets publicly available? One of the datasets available for studying Covid-19 is GISAID data (https://www.gisaid.org/) that represents million …

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

Federated Analytics & Learning Explained with Examples

federated machine learning

Federated learning is proposed as an alternative to centralized machine learning since its client-server structure provides better privacy protection and scalability in real-world applications. It is experiencing a fast boom with the wave of distributed machine learning and ever-increasing privacy concerns. With the increased computing and communicating capabilities of edge and IoT devices, applying federated learning on heterogeneous devices to train machine learning models is becoming a trend. The federated analytics approach enables extracting insights from data residing on different systems without requiring the data to be brought to the central location. By leveraging these different data sources, federated analytics can provide powerful insights in relation to different areas such …

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Posted in Machine Learning. Tagged with , .

12 Bayesian Machine Learning Applications Examples

bayesian machine learning appplications examples

Bayesian machine learning is one of the most powerful tools in data analytics. Bayes’ theorem, which was first introduced by Reverend Thomas Bayes in 1764, provides a way to infer probabilities from observations. Bayesian machine learning has become increasingly popular because it can be used for real-world applications such as credit card fraud detection and spam filtering. In this blog post, we will discuss Bayesian machine learning real-world examples to help you understand how Bayes’ theorem works. Bayesian machine learning utilizes Bayes’ theorem to predict occurrences. Bayesian inference is grounded in Bayes’ theorem, which allows for accurate prediction when applied to real-world applications. Here are some great examples of real-world …

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Posted in Bayesian, Machine Learning. Tagged with , .

Hidden Markov Models Explained with Examples

hidden markov model

Hidden Markov models (HMMs) are a type of statistical modeling that has been used for several years. They have been applied in different fields such as medicine, computer science, and data science. The Hidden Markov model (HMM) is the foundation of many modern-day data science algorithms. It has been used in data science to make efficient use of observations for successful predictions or decision-making processes. This blog post will cover hidden Markov models with real-world examples and important concepts related to hidden Markov models. What are Markov Models? Markov models are statistical models that are used to predict the next state based on the current hidden or observed states. Markov …

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Posted in Data Science, Python. Tagged with .

CNN Basic Architecture for Classification & Segmentation

image classification object detection image segmentation

Convolutional neural networks (CNNs) are deep neural networks that have the capability to classify and segment images. CNNs can be trained using supervised or unsupervised machine learning methods, depending on what you want them to do. CNN architectures for classification and segmentation include a variety of different layers with specific purposes, such as a convolutional layer, pooling layer, fully connected layers, dropout layers, etc. In this blog post, we will go over how CNNs work in detail for classification and segmentation problems. Description of basic CNN architecture for Classification The CNN architecture for classification includes convolutional layers, max-pooling layers, and fully connected layers. Convolution and max-pooling layers are used for …

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

Graph Neural Networks Explained with Examples

Training a graph neural network model

Graph neural networks (GNNs) are a relatively new area in the field of deep learning. They arose from graph theory and machine learning, where the graph is a mathematical structure that models pairwise relations between objects. Graph Neural Networks are able to learn graph structures for different data sets, which means they can generalize well to new datasets – this makes them an ideal choice for many real-world problems like social network analysis or financial risk prediction. This post will cover some of the key concepts behind graph neural networks with the help of multiple examples. What are graph neural networks (GNNs)? Graphs are data structures which are used to …

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