Author Archives: Ajitesh Kumar
Generative Modeling in Machine Learning: Examples
Machine learning has rapidly evolved over the past few years, with new techniques and methods emerging regularly. One of the most exciting and promising areas in this field is generative modeling. Generative modeling refers to the creation of new data samples that are similar to existing data sets. This technique has gained immense popularity in recent times due to its ability to generate highly realistic images, videos, and music. As a data scientist, it is crucial to understand generative modeling and its various applications. This powerful tool has been used in a wide range of fields, including computer vision, natural language processing (NLP), and even drug discovery. By learning generative …
Data Analytics Training Program (Beginners)
Data analytics has become an integral part of businesses today, helping organizations make data-driven decisions that drive success. To become proficient in data analytics and solve complex business problems, it is essential to have a strong foundation in the key concepts and tools of data analytics. My online courses, which cover topics such as data-driven decision making / decision science, business statistics, python programming, machine learning, and business analytics, are designed to help learners of all levels become experts in these areas. Check out this page for detailed information: Become Data Analytics Pro! Each of these courses is designed to help learners acquire the skills and knowledge necessary to succeed …
Histogram Plots using Matplotlib & Pandas: Python
Histograms are a graphical representation of the distribution of data. In Python, there are several ways to create histograms. One popular method is to use the Matplotlib library. In this tutorial, we will show you how to create different types histogram plots in Python using Matplotlib. As data scientists, it is important to learn how to create visualizations to communicate our findings. Histograms are one way to do this effectively. What are Histogram plots? Histogram plots are a way of representing the distribution of data. A histogram is made up of bars, with each bar representing a certain range of data values. The height of the bar indicates how many …
Generative vs Discriminative Models: Examples
The field of machine learning is rapidly evolving, and with it, the concepts and techniques that are used to develop models that can learn from data. Among these concepts, generative and discriminative models are two widely used approaches in the field. Generative models learn the joint probability distribution of the input features and output labels, whereas discriminative models learn the conditional probability distribution of the output labels given the input features. While both models have their strengths and weaknesses, understanding the differences between them is crucial to developing effective machine learning systems. Real-world problems such as speech recognition, natural language processing, and computer vision, require complex solutions that are able …
NLP Pre-trained Models: Concepts, Examples
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? …
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 learning. …
Pandas Dataframe: How to Add Rows & Columns
Pandas is a popular data manipulation library in Python, widely used for data analysis and data science tasks. Pandas Dataframe is a two-dimensional labeled data structure with columns of potentially different types, similar to a spreadsheet or SQL table. One of the common tasks in data manipulation is adding new rows or columns to an existing dataframe. It might seem like a trivial task, but choosing the right method to add rows or columns can significantly impact the performance and efficiency of your code. In this blog, we will explore the different ways to add rows and columns to a Pandas Dataframe. We will look into different methods available in …
When to Use Z-test vs T-test: Differences, Examples
When it comes to statistical tests, z-test and t-test are two of the most commonly used. But what is the difference between z-test and t-test? And when should you use Z-test vs T-test? In this blog post, we will answer all these questions and more! We will start by explaining the difference between z-test and t-test in terms of their formulas. Then we will go over some examples so that you can see how each test is used in practice. As data scientists, it is important to understand the difference between z-test and t-test so that you can choose the right test for your data. Let’s get started! Difference between …
Demystifying Encoder Decoder Architecture & Neural Network
In the field of AI / machine learning, the encoder-decoder architecture is a widely-used framework for developing neural networks that can perform natural language processing (NLP) tasks such as language translation, etc. This architecture involves a two-stage process where the input data is first encoded into a fixed-length representation, which is then decoded to produce an output that matches the desired format. As a data scientist, understanding the encoder-decoder architecture and its underlying neural network principles is crucial for building sophisticated models that can handle complex data sets. By leveraging encoder-decoder neural network architecture, data scientists can design neural networks that can learn from large amounts of data, accurately classify …
Neural Network Types & Real-life Examples
Neural networks are a powerful tool for data scientists, machine learning engineers, and statisticians. They have revolutionized the field of machine learning and have become an integral part of many real-world applications such as image and speech recognition, natural language processing, and autonomous vehicles. ChatGPT is a classic example how AI / neural network applications has taken world by storm. But what exactly are they and what are their different types? There are various types of neural networks, each with their own unique architecture and learning algorithm. Understanding the different types of neural networks and their real-life examples is crucial for anyone interested in machine learning and artificial intelligence. In …
Sequence to Sequence Models: Types, Examples
Sequence to sequence (Seq2Seq) modeling is a powerful machine learning technique that has revolutionized the way we do natural language processing (NLP). It allows us to process input sequences of varying lengths and produce output sequences of varying lengths, making it particularly useful for tasks such as language translation, speech recognition, and chatbot development. Sequence to sequence modeling also provides a great foundation for creating text summarizers, question answering systems, sentiment analysis systems, and more. With its wide range of applications, learning about sequence to sequence modeling concepts is essential for anyone who wants to work in the field of natural language processing. This blog post will discuss types of …
Natural Language Processing (NLP) Task Examples
Have you ever wondered how your phone’s voice assistant understands your commands and responds appropriately? Or how search engines are able to provide relevant results for your queries? The answer lies in Natural Language Processing (NLP), a subfield of artificial intelligence (AI) that focuses on enabling machines to understand and process human language. NLP is becoming increasingly important in today’s world as more and more businesses are adopting AI-powered solutions to improve customer experiences, automate manual tasks, and gain insights from large volumes of textual data. With recent advancements in AI technology, it is now possible to use pre-trained language models such as ChatGPT to perform various NLP tasks with …
Types of Data Visualization: Examples
In today’s data-driven world, the ability to extract insights from vast amounts of information has become a critical skill for data scientists and analysts. Visualizing data through charts, graphs, and other types of visual representations can help them uncover patterns and relationships that might be difficult to spot otherwise. However, not all visualizations are created equal, and choosing the right type of visualization can make all the difference in communicating insights effectively. That’s why understanding the different types of visualization available is crucial for data visualization experts and data scientists. In this blog, we’ll explore some of the most common types of visualization, including comparison plots, relation plots, composition plots …
Analytics Maturity Model for Analytics COE
As businesses continue to embrace digital transformation, data analytics has become an essential component of business strategy. As a result, many organizations have established or are in the process of setting up an Analytics Center Of Excellence (Analytics COE) to drive analytics initiatives and improve their overall data-driven decision-making capabilities. TO achieve this objective, analytics COE requires a framework for assessing the maturity of their analytics practice. This is where analytics maturity model kicks in. The Analytics Maturity Model can be defined as a framework that enables organizations to assess their current analytics capabilities, identify areas for improvement, and establish a roadmap for analytics maturity. It provides a structured approach …
Hypothesis Testing in Business: Examples
Are you a product manager or data scientist looking for ways to identify and use most appropriate hypothesis testing for understanding business problems and creating solutions for data-driven decision making? Hypothesis testing is a powerful statistical technique that can help you understand problems during exploratory data analysis (EDA) and identify most appropriate hypotheses / analytical solution. In this blog, we will discuss hypothesis testing with examples from business. We’ll also give you tips on how to use it effectively in your own problem-solving journey. With this knowledge, you’ll be able to confidently create hypotheses, run experiments, and analyze the results to derive meaningful conclusions. So let’s get started! Before going …
Hypothesis Testing Steps & Examples
Hypothesis testing is a technique that helps scientists, researchers, or for that matter, anyone test the validity of their claims or hypotheses about real-world or real-life events in order to establish new knowledge. Hypothesis testing techniques are often used in statistics and data science to analyze whether the claims about the occurrence of the events are true, whether the results returned by performance metrics of machine learning models are representative of the models or they happened by chance. This blog post will cover some of the key statistical concepts including steps and examples in relation to what is hypothesis testing, how to formulate them and how to use them in …
