Author Archives: Ajitesh Kumar

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about 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. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking

What is Blockchain & How does it work?

Decentralized vs centralized ledger or database

Blockchain is a distributed database that allows for secure, transparent, and tamper-proof transactions. It was first introduced in 2009 as the underlying technology behind Bitcoin. Blockchain has since garnered a great deal of attention due to its potential applications in a variety of industries. In this blog post, we will explore what blockchain is and how it works! What is Blockchain? Blockchain is a distributed database that allows for secure, transparent, and tamper-proof transactions. Blockchain was originally conceived as the underlying technology for the cryptocurrency bitcoin. However, Blockchain has since been found to have many other potential use cases. The key concepts behind Blockchain are decentralization, immutability, and consensus. Blockchain …

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Different Types of CNN Architectures Explained: Examples

VGG16 CNN Architecture

The CNN architectures are the most popular deep learning framework. CNNs are used for a variety of applications, ranging from computer vision to natural language processing. In this blog post, we will discuss each type of CNN architecture in detail and provide examples of how these models work. Even before we get to learn about the different types of CNN architecture, let’s briefly recall what is CNN in the first place? What is CNN? CNNs are a type of deep learning algorithm that are used to process data with a grid-like topology. CNNs are a type of deep learning algorithm that is used to process data that has a spatial …

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

Probability: Basic concepts, formulas, and examples

probability concepts formula and examples

Probability is a branch of mathematics that deals with the likelihood of an event occurring. It is important to understand probability concepts if you want to get good at data science and machine learning. In this blog post, we will discuss the basic concepts of probability and provide examples to help you understand it better. We will also introduce some common formulas associated with probability. So, let’s get started! What is probability and what are the different types? Probability is a concept in mathematics that measures the likelihood of an event occurring. It is typically expressed as a number between 0 and 1, with 0 indicating that an event is …

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Posted in Data Science, Mathematics. Tagged with , .

When to Use Which Clustering Algorithms?

when to use which clustering algorithm

There are many clustering machine learning algorithms to choose from when you want to cluster data. But which one should you use in a particular situation? In this blog post, we will explore the different clustering algorithms and explain when each one is most appropriate. We will also provide examples so that you can see how these algorithms work in practice. What clustering is and why it’s useful Simply speaking, clustering is a technique used in machine learning to group data points together. The goal of clustering is to find natural groups, or clusters, in the data. Clustering algorithms are used to automatically find these groups. Clustering is useful because …

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

Key Challenges for Data Science / AI Projects Implementation

Challenges related to Machine Learning Projects Implementations

In this post, you will learn about some of the key challenges in relation to achieving successful AI / machine learning (ML) or Data science projects implementation in a consistent and sustained manner. As AI / ML project stakeholders including senior management stakeholders, data science architects, product managers, etc, you must get a good understanding of what would it take to successfully execute AI / ML projects and create value for the customers and the business.  Whether you are building AI / ML products or enabling unique models for your clients in SaaS setup, you will come across most of these challenges.  Understanding the problem Many times, the nature of …

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Posted in AI, Machine Learning. Tagged with , .

Accuracy, Precision, Recall & F1-Score – Python Examples

Classification models are used in classification problems to predict the target class of the data sample. The classification model predicts the probability that each instance belongs to one class or another. It is important to evaluate the performance of the classifications model in order to reliably use these models in production for solving real-world problems. Performance measures in machine learning classification models are used to assess how well machine learning classification models perform in a given context. These performance metrics include accuracy, precision, recall, and F1-score. Because it helps us understand the strengths and limitations of these models when making predictions in new situations, model performance is essential for machine …

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

AI / Data Science Operating Model: Teams, Processes

data science operating model

Realizing value from AI/data science or machine learning projects requires the coordination of many different teams based on an appropriate operating model. If you want to build an effective AI/data science operation, you need to create a data science operating model that outlines the steps involved in how teams are structured, how data science projects are implemented, how the maturity of data science practice is evaluated and an overall governance model which is used to keep a track of data science initiatives. In this blog post, we will discuss the key components of a data science operating model and provide examples of how to optimize your data science process. AI …

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Posted in Data analytics, Data Science. Tagged with .

Difference between Online & Batch Learning

online learning - machine learning system

In this post, you will learn about the concepts and differences between online and batch or offline learning in relation to how machine learning models in production learn incrementally from the stream of incoming data or otherwise. It is one of the most important aspects of designing machine learning systems. Data science architects would require to get a good understanding of when to go for online learning and when to go for batch or offline learning. Why online learning vs batch or offline learning? Before we get into learning the concepts of batch and on-line or online learning, let’s understand why we need different types of models training or learning …

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

Deductive & Inductive Reasoning: Examples, Differences

inductive vs deductive reasoning

When it comes to data science, there are two main types of reasoning that you need to be familiar with: deductive and inductive. Both of these techniques are important in order to make sound decisions based on the data that you’re working with. In this blog post, we’ll take a closer look at what deductive and inductive reasoning are, what are their differences, and how they’re related to each other. What is deductive reasoning? Deductive reasoning is an important tool in data science. Deductive reasoning is the process of deriving a conclusion based on premises that are known or assumed to be true. In other words, deductive reasoning allows you …

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What is Data-Driven Decision-Making? Why & How?

data driven decision making what why how

Data-driven decision-making is a data-driven approach to making decisions including business 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 actionable insights generated from the data. The goal is to make informed decisions while ensuring transparency across the stakeholders. 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! Before we dive in and understand what is data-driven decision-making, lets understand what are first principles of decision-making? What are …

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

What is Data Quality Management? Concepts & Examples

what is data quality and why is it important

What is data quality? This is a question that many people ask, but it is not always easy to answer. Simply put, data quality refers to the accuracy and completeness of data. If data is not accurate, it can lead to all sorts of problems for businesses. That’s why data quality is so important – it ensures that your data is reliable and can be used for decision-making purposes. Data is at the heart of any enterprise. It is essential for making sound business decisions, understanding customers, and improving operations. However, not all data is created equal. In order to make the most out of your data, you need to …

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Posted in Data, Data analytics. Tagged with , .

Steps for Evaluating & Validating Time-Series Models

evaluating and validating time-series models

Time-series machine learning models are becoming increasingly popular due to the large volume of data that is now available. These models can be used to make predictions about future events, and they are often more accurate than traditional methods. However, it is important to properly evaluate (check accuracy by performing error analysis) and validate these models before you put them into production. In this blog post, we will discuss the different ways that you can evaluate and validate time series machine learning models. We will also provide some tips on how to improve your results. As data scientists, it is important to learn the techniques related to evaluating time-series models. …

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Logit vs Probit Models: Differences, Examples

Logit vs probit models

Logit and probit models are statistical models that are used to model binary or dichotomous dependent variables. This means that the outcome of interest can only take on two possible values. In most cases, these models are used to predict whether or not something will happen. For example, a business might want to know if a particular advertising campaign will lead to an increase in sales. In this blog post, we will explain what logit and probit models are, and we will provide examples of how they can be used. As data scientists, it is important to understand the concepts of logit and probit models and when should they be …

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

Linear vs Logistic Regression: Differences, Examples

simple linear regression model 1

Linear regression and logistic regression are both methods for modeling relationships between variables. They are both used to build statistical models but perform different tasks. Linear regression is used to model linear relationships, while logistic regression is used to model binary outcomes (i.e. whether or not an event happened). In this blog post, we will discuss the differences between linear and logistic regression, as well as when to use each one. We will also provide examples so that you can understand how they work. What is linear regression? Linear regression is used to solve the regression problems. It is used to model linear relationships. This means that it can be …

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

What is Explainable AI? Concepts & Examples

Explainable AI - SHAP Explainability

What is explainable AI (XAI)? This is a question that more people are asking, as they become aware of the potential implications of artificial intelligence. Simply put, explainable AI is the form of AI that can be understood by humans. It is AI that provides an explanation for its decisions and actions. 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. This is important, as many people are concerned about the increasing use of AI in our lives, especially in healthcare. …

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Random Forest Classifier Python Example

random forest classifier machine learning

Random forest classifiers are popular machine learning algorithms that are used for classification. In this post, you will learn about the concepts of random forest classifiers and how to train a Random Forest Classifier using the Python Sklearn library. This code will be helpful if you are a beginner data scientist or just want to quickly get a code sample to get started with training a machine learning model using the Random Forest algorithm. The following topics will be covered: What is a random forest classifier & how do they work? Random forests are a type of machine learning algorithm that is used for classification and regression tasks. A classifier …

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