Blog

• 3 Ways of Using Gemma 2 Locally August 20, 2024
  After the highly successful launch of Gemma 1, the Google team introduced an even more advanced model series called Gemma 2. This new family of Large Language Models (LLMs) includes models with 9 billion (9B) and 27 billion (27B) parameters. Gemma 2 offers higher performance and greater inference efficiency than its predecessor, with significant safety […]
  Abid Ali Awan
• 7 Machine Learning Projects That Can Add Value to Any Resume August 19, 2024
  Learning by doing is the best way to master essential skills for becoming a machine learning engineer. Instead of just focusing on simple classification and regression models. In this blog, we will focus on advanced machine learning projects that will impact your resume and attract recruiters and hiring managers. We will learn about computer vision […]
  Abid Ali Awan
• One Hot Encoding: Understanding the “Hot” in Data August 18, 2024
  Preparing categorical data correctly is a fundamental step in machine learning, particularly when using linear models. One Hot Encoding stands out as a key technique, enabling the transformation of categorical variables into a machine-understandable format. This post tells you why you cannot use a categorical variable directly and demonstrates the use One Hot Encoding in […]
  Vinod Chugani
• The Search for the Sweet Spot in a Linear Regression with Numeric Features August 14, 2024
  Consistent with the principle of Occam’s razor, starting simple often leads to the most profound insights, especially when piecing together a predictive model. In this post, using the Ames Housing Dataset, we will first pinpoint the key features that shine on their own. Then, step by step, we’ll layer these insights, observing how their combined […]
  Vinod Chugani
• Free Tools Every ML Beginner Should Use August 13, 2024
  We have all experienced it: starting is the toughest part of any journey. So getting started in the ML field wouldn’t be any different. This is why today I want to highlight some of the essential tools that every beginner — or person willing to get started — with ML should be using. Jupyter Notebook […]
  Josep Ferrer
• 5 Free Podcasts That Demystify Machine Learning Concepts August 12, 2024
  Machine learning (ML) has become a buzzword in recent years, with applications ranging from voice assistants to self-driving cars. Yet, for many, the inner workings of these technologies remain a mystery. Podcasts offer a great way to learn about this field without getting overwhelmed. They break down complex ideas into simpler terms and let you […]
  Nahla Davies
• Building a Simple RAG Application Using LlamaIndex August 12, 2024
  In this tutorial, we will explore Retrieval-Augmented Generation (RAG) and the LlamaIndex AI framework. We will learn how to use LlamaIndex to build a RAG-based application for Q&A over the private documents and enhance the application by incorporating a memory buffer. This will enable the LLM to generate the response using the context from both […]
  Abid Ali Awan
• The Strategic Use of Sequential Feature Selector for Housing Price Predictions August 12, 2024
  To understand housing prices better, simplicity and clarity in our models are key. Our aim with this post is to demonstrate how straightforward yet powerful techniques in feature selection and engineering can lead to creating an effective, simple linear regression model. Working with the Ames dataset, we use a Sequential Feature Selector (SFS) to identify […]
  Vinod Chugani
• 5 Tips for Getting Started with Time Series Analysis August 9, 2024
  As a machine learning engineer or a data scientist, you’ll likely need to work with time series data. Time series analysis focuses on data indexed by time, such as stock prices, temperature, and the like. If you’re already comfortable with machine learning fundamentals but new to time series, this guide will provide you with five […]
  Bala Priya C
• From Train-Test to Cross-Validation: Advancing Your Model’s Evaluation August 7, 2024
  Many beginners will initially rely on the train-test method to evaluate their models. This method is straightforward and seems to give a clear indication of how well a model performs on unseen data. However, this approach can often lead to an incomplete understanding of a model’s capabilities. In this blog, we’ll discuss why it’s important […]
  Vinod Chugani