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Of Muffins and Machine Learning Models

Cloudera

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g.

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50 ML Projects To Strengthen Your Portfolio and Get You Hired

ProjectPro

The most trusted way to learn and master the art of machine learning is to practice hands-on projects. Projects help you create a strong foundation of various machine learning algorithms and strengthen your resume. Each project explores new machine learning algorithms, datasets, and business problems.

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15+ Machine Learning Projects for Resume with Source Code

ProjectPro

Sending out the exact old traditional style data science or machine learning resume might not be doing any favours in your machine learning job search. With cut-throat competition in the industry for high-paying machine learning jobs, a boring cookie-cutter resume might not just be enough. You would add it in a jiffy, right?

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20 Python Projects for Data Science in 2023

ProjectPro

Table of Contents Why Learn Python for Data Science? Top 20 Python Projects for Data Science Getting Started with Python for Data Science FAQs about data science projects Why Learn Python for Data Science? It is loved by all data enthusiasts and provides an easy introduction to data science and machine learning.

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100+ Machine Learning Datasets Curated For You

ProjectPro

Undoubtedly, everyone knows that the only best way to learn data science and machine learning is to learn them by doing diverse projects. But yes, there is definitely no other alternative to data science and machine learning projects. Table of Contents What is a dataset in machine learning?

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Propensity Model: How to Predict Customer Behavior Using Machine Learning

AltexSoft

It’s a common practice for companies and their marketing teams to try guessing how likely certain groups of customers are going to act under certain circumstances. For this purpose, they create propensity models. Built in a traditional statistical fashion, the accuracy of outcomes predictive tools provide isn’t always high.