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I Won $10,000 in a Machine Learning Competition — Here’s My Complete Strategy

Flipboard

The world’s leading publication for data science, AI, and ML professionals. Getting Started: You Don’t Need Expensive Hardware Let me get this clear, you don’t necessarily need an expensive cloud computing setup to win ML competitions (unless the dataset is too big to fit locally).

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LLMOps demystified: Why it’s crucial and best practices for 2023

Data Science Dojo

Exploratory Data Analysis (EDA) Data collection: The first step in LLMOps is to collect the data that will be used to train the LLM. Exploratory Data Analysis (EDA): Continuously prepare and explore data for the machine learning lifecycle, creating shareable visualizations and reproducible datasets.

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11 Open Source Data Exploration Tools You Need to Know in 2023

ODSC - Open Data Science

ydata-profiling GitHub | Website The primary goal of ydata-profiling is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution. Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst.

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How to Work Smarter, Not Harder, with Artificial Intelligence

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To excel in ML, you must understand its key methodologies: Supervised Learning: Involves training models on labeled datasets for tasks like classification (e.g., These techniques allow you to select the most effective approach for addressing specific challenges, making ML expertise indispensable in AI development.

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Data Acquisition & Exploration: Exploring 5 Key MLOps Questions using AWS SageMaker

Towards AI

Last Updated on June 28, 2023 by Editorial Team Author(s): Anirudh Mehta Originally published on Towards AI. The ’31 Questions that Shape Fortune 500 ML Strategy’ highlighted key questions to assess the maturity of an ML system. A robust ML platform offers managed solutions to easily address these aspects.

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Roadmap to Learn Data Science for Beginners and Freshers in 2023

Becoming Human

For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis. First learn the basics of Feature Engineering, and EDA then take some different-different data sheets (data frames) and apply all the techniques you have learned to date.

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31 Questions that Shape Fortune 500 ML Strategy

Towards AI

Last Updated on June 14, 2023 by Editorial Team Author(s): Anirudh Mehta Originally published on Towards AI. As such, my intention with this blog is not to duplicate those definitions but rather to encourage you to question and evaluate your current ML strategy. Source: Image by the author. Source: Image by the author.

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