One of the significant challenges statisticians and data scientists face is multicollinearity, particularly its most severe form, perfect multicollinearity. This issue often lurks undetected in large datasets with many features, potentially disguising itself and skewing the results of statistical models. In this post, we explore the methods for detecting, addressing, and refining models affected by
The post Detecting and Overcoming Perfect...
When we analyze relationships between variables in machine learning, we often find that a straight line doesn’t tell the whole story. That’s where polynomial transformations come in, adding layers to our regression models without complicating the calculation process. By transforming our features into their polynomial counterparts—squares, cubes, and other higher-degree terms—we give linear models the
The post Capturing...
As we progress through 2024, machine learning (ML) continues to evolve at a rapid pace. Python, with its rich ecosystem of libraries, remains at the forefront of ML development. In this post, we’ll explore the top 10 Python libraries dominating the ML scene in 2024, how the field has changed since 2020, and the key
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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
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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
The post The Search for the Sweet Spot in a Linear Regression with Numeric Features...
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
The post One Hot Encoding: Understanding the “Hot” in Data...
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
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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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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
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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
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