Tag: AI/ML Techniques

Hugging Face has significantly contributed to the breakthrough of machine learning application technology, especially in the NLP field. They could contribute a lot because Hugging Face focuses on building a platform for the community to easily access models, tools, and datasets to the public. That’s why Hugging Face has become a place to contribute to …

Read more “Everything You Need to Know About the Hugging Face Model Hub and Community”

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 …

Read more “10 Must-Know Python Libraries for Machine Learning in 2024”

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 …

Read more “Capturing Curves: Advanced Modeling with Polynomial Regression”

Bayesian statistics constitute one of the not-so-conventional subareas within statistics, based on a particular vision of the concept of probabilities. This post introduces and unveils what bayesian statistics is and its differences from frequentist statistics, through a gentle and predominantly non-technical narrative that will awaken your curiosity about this fascinating topic. Introduction Statistics constitutes an …

Read more “A Gentle Introduction to Bayesian Statistics”

Linear regression models are foundational in machine learning. Merely fitting a straight line and reading the coefficient tells a lot. But how do we extract and interpret the coefficients from these models to understand their impact on predicted outcomes? This post will demonstrate how one can interpret coefficients by exploring various scenarios. We’ll delve into …

Read more “Interpreting Coefficients in Linear Regression Models”

Introduction Statistical analysis is important in data science. It helps us understand data better. NumPy is a key Python library for numerical operations. It simplifies and speeds up this process. In this article, we will explore several functions for basic statistical analysis offered by NumPy. NumPy is a Python library for numerical computing. It helps …

Read more “Basic Statistical Analysis with NumPy”

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 …

Read more “3 Ways of Using Gemma 2 Locally”

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”