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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 …
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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 …
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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 …
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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 …
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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”
If you’re familiar with machine learning, you know that the training process allows the model to learn the optimal values for the parameters—or model coefficients—that characterize it. But machine learning models also have a set of hyperparameters whose values you should specify when training the model. So how do you find the optimal values for …
Read more “Tips for Tuning Hyperparameters in Machine Learning Models”
Statistics and Machine Learning both aim to extract insights from data, though their approaches differ significantly. Traditional statistics primarily concerns itself with inference, using the entire dataset to test hypotheses and estimate probabilities about a larger population. In contrast, machine learning emphasizes prediction and decision-making, typically employing a train-test split methodology where models learn from …
Read more “Integrating Scikit-Learn and Statsmodels for Regression”
Large Language Models (LLMs) are a hot topic right now, and everyone is getting involved in this new trend. Companies are searching for LLM engineers who can develop and implement AI solutions to optimize their workflow and reduce costs through automation, customer service, recommendations, issue resolution, and debugging. Instead of worrying that AI will take …
If you’re new to machine learning, understanding basic terms is crucial. Knowing key terms can help you understand the basics better. Here are 7 essential terms every beginner should know. These terms will give you a solid foundation to build your machine learning knowledge. 1. Algorithm An algorithm is a set of rules a computer …
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