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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 …
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 …
Read more “7 Machine Learning Projects That Can Add Value to Any Resume”
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 …
Read more “One Hot Encoding: Understanding the “Hot” in Data”
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 …
Read more “The Search for the Sweet Spot in a Linear Regression with Numeric Features”
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 …
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 …
Read more “5 Free Podcasts That Demystify Machine Learning Concepts”
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 …
Read more “Building a Simple RAG Application Using LlamaIndex”
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 …
Read more “The Strategic Use of Sequential Feature Selector for Housing Price Predictions”
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 …
Read more “5 Tips for Getting Started with Time Series Analysis”
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 …
Read more “From Train-Test to Cross-Validation: Advancing Your Model’s Evaluation”