Tag Archives: machine learning
AIC & BIC for Selecting Regression Models: Formula, Examples
Are you grappling with the complexities of choosing the right regression model for your data? You are not alone. When working with regression models, selecting the most appropriate machine learning model is a critical step toward understanding the relationships between variables and making accurate predictions. With numerous regression models available, it becomes essential to employ robust criteria for model selection. This is where the two most widely used criteria come to the rescue. They are the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). In this blog, we will learn about the concepts of AIC, BIC and how they can be used to select the most appropriate machine …
Problems with Categorical Variables: Examples
Have you ever encountered unfamiliar words while learning a new language and didn’t know their meanings? Or tried to fit all your belongings into a suitcase, only to realize it’s too full? Or started reading a book series from the third book and felt lost? These scenarios in our daily lives surprisingly resemble some challenges we face with categorical variables in machine learning. Categorical variables, while essential in many datasets, bring with them a unique set of challenges. In this article, we’ll be discussing three major problems associated with categorical features: Let’s explore each with real-life examples and supporting Python code snippets. Incomplete Vocabulary The “Incomplete Vocabulary” problem arises when …
Feature Engineering in Machine Learning: Python Examples
Have you ever wondered why some machine learning models perform exceptionally well while others don’t? Could the magic ingredient be something other than the algorithm itself? The answer is often “Yes,” and the magic ingredient is feature engineering. Good feature engineering can make or break a model. In this blog, we will demystify various techniques for feature engineering, including feature extraction, encoding categorical variables, feature scaling, and feature selection. To demonstrate these methods, we’ll be using a real-world dataset containing car sales data. This dataset includes a variety of features such as ‘Company Name’, ‘Model Name’, ‘Price’, ‘Model Year’, ‘Mileage’, and more. Through this dataset, we’ll explore how to improve …
Insurance & Linear Regression Model Example
Ever wondered how insurance companies determine the premiums you pay for your health insurance? Predicting insurance premiums is more than just a numbers game—it’s a task that can impact millions of lives. In this blog, we’ll demystify this complex process by walking you through an end-to-end example of predicting health insurance premium charges by demonstrating with Python code example. Specifically, we’ll use a linear regression model to predict these charges based on various factors like age, BMI, and smoking status. Whether you’re a beginner in data science or a seasoned professional, this blog will offer valuable insights into building and evaluating regression models. What is Linear Regression? Linear Regression is …
Text Clustering Real-World Applications: Examples
How often have you wondered about the vast amounts of unstructured data around us and its untapped potential? How can businesses sift through thousands of customer reviews, documents, or feedback to derive actionable insights? What if there was a way to automatically group similar pieces of text, helping organizations quickly identify patterns and trends? Enter text clustering. A subset of text analytics, text clustering is an unsupervised machine learning task that divides a set of texts into clusters or groups. This ensures that texts in the same group are more similar to each other than to those in other groups. A powerful tool for deciphering insights from unstructured data, text …
Linear Regression Python Examples
In this post, you will learn about concepts of linear regression along with Python Sklearn examples for training linear regression models. Linear regression belongs to a class of parametric models and is used to train supervised machine learning models. Introduction to Linear Regression Linear regression is a machine learning algorithm used to predict the value of continuous response variables. The predictive analytics problems that are solved using linear regression models are called supervised learning problems as they require that the value of response/target variables must be present and used for training the models. Also, recall that “continuous” represents the fact that the response variable is numerical in nature and can take infinite different values. …
Find Topics of Text Clustering: Python Examples
Have you ever clustered a collection of texts and wondered what predominant topics underlie each group? How can you pinpoint the essence of each cluster comprising of large volume of words? Is there a way to succinctly represent the core topic of each cluster using Python? Text clustering is a powerful technique in natural language processing (NLP) that groups documents into clusters based on their content. Once you’ve clustered your data, a natural follow-up question arises: “What are these clusters about?” In this article, we’ll discuss two different methods to find the dominant topics of text clusters using Python. Meanwhile, check out my post on text clustering – Text Clustering …
OpenAI Python API Example for NLP Tasks
Ever wondered how you can leverage the power of OpenAI’s GPT-3 and GPT-3.5 (from Jan 2024 onwards) directly in your Python application? Are you curious about generating human-like text with just a few lines of code? This blog post will walk you through an example Python code snippet that utilizes OpenAI’s Python API for different NLP tasks such as text generation. Check out my other post on how to use Langchain framework for text generation using OpenAI GPT models. OpenAI Python APIs The OpenAI Python API is an interface that allows you to interact with OpenAI’s language models, including their GPT-3 model. The following are different popular models that you …
Architecting a Generative AI Platform for GPT-based LLM Apps
Have you ever wondered how to build a scalable Generative AI platform based on OpenAI GPT models that can serve different applications? Are you a data scientist, product manager, or software engineer looking to understand the intricacies of the architecture of such a scalable generative AI platform? This blog aims to demystify the architectural building blocks needed to create a robust GPT-based platform. By the end, you will have a clear roadmap for architecting, designing, and implementing your own GPT-based large language models (LLMs) applications platform. Generative AI Platform Architecture for GPT-based LLM Apps The following is the technology architecture of generative AI platform which can leverage OpenAI GPT based …
Microsoft’s Free Courses: Data Science, Machine Learning, AI
Are you keen on diving into the world of data science, machine learning, or artificial intelligence? Have you been searching for courses that not only teach the fundamentals but are also free and accessible? Look no further! Microsoft has put together three distinct courses that will cater to your interests and ignite your passion for learning. Data Science for Beginners This course offers an ideal starting point for those new to data science, focusing on the basics and guiding through practical exercises. The course would help you demystify the complex world of data, allowing you to make informed decisions in various fields such as business, healthcare, and more. Each lesson …
Text Clustering Python Examples: Steps, Algorithms
Text clustering has swiftly emerged as a cornerstone in data-driven decision-making across industries. But what exactly is text clustering, and how can it transform the way businesses operate? How does it convert unstructured text into actionable insights? What are the core steps involved in text clustering, and how are they interlinked? What algorithms are pivotal in implementing text clustering effectively? In this blog, we will unravel these questions, diving deep into the systematic steps of text clustering, its underlying algorithms, and real-world examples that bring this technique to life. Whether you’re a product manager seeking to leverage data analytics or a data scientist curious to learn key steps of text …
Topic Modeling LDA Python Example
Are you overwhelmed by the endless streams of text data and looking for a way to unearth the hidden themes that lie within? Have you ever wondered how platforms like Google News manage to group similar articles together, or how businesses extract insights from vast volumes of customer reviews? The answer to these questions might be simpler than you think, and it’s rooted in the world of Topic Modeling. Introducing Latent Dirichlet Allocation (LDA) – a powerful algorithm that offers a solution to the puzzle of understanding large text corpora. LDA is not just a buzzword in the data science community; it’s a mathematical tool that has found applications in …
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 …
Huggingface Arxiv Dataset: Python Example
Working with large and specific datasets is a common requirement in the field of natural language processing (NLP) and machine learning. The Arxiv dataset, containing metadata such as titles, abstracts, years, and categories of research papers, is an invaluable resource for researchers and data scientists. How can we easily load this dataset and extract the required information? In this blog post, we will explore a Python example using the Hugging Face library to load the Arxiv dataset and extract specific metadata. Python Code for Loading Huggingface Arxiv Dataset The following are the steps to load Hugging face Arxiv dataset using python code: Real-World Application Use Cases: Analyzing Research Papers Imagine …
Transformer Architecture Types: Explained with Examples
Are you fascinated by the power of deep learning models that can translate languages, generate creative writing, and even answer complex questions? Ever wondered how a machine can understand and process human language with such finesse? At the heart of these remarkable achievements lies a machine learning model architecture that has revolutionized the field of Natural Language Processing (NLP) – the Transformer architecture, a deep learning architecture. But what makes Transformer models so special? How do they manage to encode the subtle nuances of language and context? Can we understand the complex mathematical machinery that operates behind the scenes? Whether you’re a seasoned data scientist, an aspiring machine learning engineer, …
Quiz: BERT & GPT Transformer Models Q&A
Are you fascinated by the world of natural language processing and the cutting-edge generative AI models that have revolutionized the way machines understand human language? Two such large language models (LLMs), BERT and GPT, stand as pillars in the field, each with unique architectures and capabilities. But how well do you know these models? In this quiz blog, we will challenge your knowledge and understanding of these two groundbreaking technologies. Before you dive into the quiz, let’s explore an overview of BERT and GPT. BERT (Bidirectional Encoder Representations from Transformers) BERT is known for its bidirectional processing of text, allowing it to capture context from both sides of a word …
Nice question to help us