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The AI Process

Towards AI

In general, the results of current journal articles on AI (even peer-reviewed) are irreproducible. Data preparation: This step includes the following tasks: data preprocessing, data cleaning, and exploratory data analysis (EDA). 85% or more of AI projects fail [1][2].

AI 97
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Build a Stocks Price Prediction App powered by Snowflake, AWS, Python and Streamlit?—?Part 2 of 3

Mlearning.ai

Introduction Welcome Back, Let's continue with our Data Science journey to create the Stock Price Prediction web application. This is part 2 of the three-series article. If you are here for the first time then please check out this article first. The data is in good shape. We will perform the following steps.

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Showcasing the Power of AI in Investment Management: a Real Estate Case Study

DataRobot Blog

In this article, we’ll showcase the ability of AI to improve the quality of the potential investment’s future performance, with a specific example from the real estate segment. In this article, we’ll first take a closer look at the concept of Real Estate Data Intelligence and the potential of AI to become a game changer in this niche.

AI 59
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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

This article aims to guide you through the intricacies of Data Analyst interviews, offering valuable insights with a comprehensive list of top questions. Additionally, we’ve got your back if you consider enrolling in the best data analytics courses. In my previous role, we had a project with a tight deadline.

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Data Science Project?—?Predictive Modeling on Biological Data

Mlearning.ai

Data Science Project — Predictive Modeling on Biological Data Part III — A step-by-step guide on how to design a ML modeling pipeline with scikit-learn Functions. Photo by Unsplash Earlier we saw how to collect the data and how to perform exploratory data analysis. I hope you enjoyed this article.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. Introduction In the rapidly evolving field of Data Science, understanding key terminology is crucial for Data Analysts to communicate effectively, collaborate effectively, and drive data-driven projects.

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Large Language Models: A Complete Guide

Heartbeat

A small portion of the LLM ecosystem; image from scalevp.com In this article, we will provide a comprehensive guide to training, deploying, and improving LLMs. In this article, we will explore the essential steps involved in training LLMs, including data preparation, model selection, hyperparameter tuning, and fine-tuning.