Learner, Practitioner & Teacher
Introduction: In the ever-evolving landscape of marketing, the ability to communicate precisely with the right client at the right moment is a strategic imperative for any company. The key to success lies in personalized strategies that encourage conversion, engagement, and
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In the realm of language models, the emergence of Large Language Models (LLMs) has significantly transformed how we interact with artificial intelligence. These models, while impressive in their ability to provide answers based on extensive training data, face a critical
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Exploratory Data Analysis (EDA) is a crucial phase in any data science project, providing insights into the underlying patterns and relationships within your dataset. One powerful tool that can elevate your EDA experience is PyGWalker, a versatile Python library for
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Introduction In the rapidly evolving landscape of artificial intelligence, choosing the right approach for a given task is crucial. Two prominent methodologies, Traditional Machine Learning (ML) and Generative Artificial Intelligence (Generative AI), each have their strengths and weaknesses. In this
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Introduction: In the realm of machine learning and statistics, two fundamental analytical approaches—discriminative and generative—play pivotal roles in shaping models and decision-making processes. In this blog post, we will delve into the characteristics of discriminative and generative approaches, highlighting their
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Introduction: Data Exploratory Analysis (EDA) is a crucial step in the data science pipeline that involves understanding and visualizing the characteristics of a dataset before diving into model building. Several Python packages have been developed to streamline and enhance the
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Introduction In the ever-evolving realm of artificial intelligence, two predominant paradigms have emerged: foundational models and traditional machine learning. These approaches exhibit distinct characteristics and applications, making it crucial to understand their differences. In this blog post, we’ll conduct a
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Introduction: In the grand journey of humanity, our past generations foresaw a future centered around three pillars: Products, Knowledge, and Communication. Let’s delve into the evolution of these elements, shaping our lives from the simplest tools to the cutting-edge advancements
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There are several types of feature importance methods that can be used to explain how a machine learning model is making its predictions. Here are some of the most common types: Permutation Importance: This method measures the importance of each
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Partial dependence plots (PDP) are a type of model-agnostic XAI technique that allows us to visualize the relationship between an input feature and the model’s output. PDPs show how the predicted outcome changes as we vary the values of a
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