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7 Machine Learning Portfolio Projects to Boost the Resume

KDnuggets

Work on machine learning and deep learning portfolio projects to learn new skills and improve your chance of getting hired.

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5 Python Projects for Data Science Portfolio

KDnuggets

Get more experience by working on web scraping, data analytics, time-series forecasting, machine learning, and deep learning projects.

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Learn Deep Learning by Building 15 Neural Network Projects in 2022

KDnuggets

Here are 15 neural network projects you can take on in 2022 to build your skills, your know-how, and your portfolio.

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KDnuggets News, October 19: 3 Valuable Skills That Have Doubled My Income as a Data Scientist • The Complete Free PyTorch Course for Deep Learning

KDnuggets

3 Valuable Skills That Have Doubled My Income as a Data Scientist • The Complete Free PyTorch Course for Deep Learning • 7 Free Platforms for Building a Strong Data Science Portfolio • Mathematics for Machine Learning: The Free eBook • 25 Advanced SQL Interview Questions for Data Scientists.

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Deep Learning vs Machine Learning -What's the Difference?

ProjectPro

“Machine Learning” and “Deep Learning” – are two of the most often confused and conflated terms that are used interchangeably in the AI world. However, there is one undeniable fact that both machine learning and deep learning are undergoing skyrocketing growth. respectively.

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KDnuggets Top Posts for October 2022: 10 Cheat Sheets You Need To Ace Data Science Interview

KDnuggets

10 Cheat Sheets You Need To Ace Data Science Interview • 7 Free Platforms for Building a Strong Data Science Portfolio • The Complete Free PyTorch Course for Deep Learning • 3 Valuable Skills That Have Doubled My Income as a Data Scientist • 25 Advanced SQL Interview Questions for Data Scientists • A Data Science Portfolio That Will Land You The Job (..)

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Mastering Data Science in 2024 [A Beginner's Guide]

Knowledge Hut

Get Familiar with Applied Mathematics In machine learning and data science, mathematics isn't about crunching numbers; it's about knowing what's happening, why, and how we may try different variables to get the outcomes we want. If you're more interested in the technical side of statistics, you might not have to learn Math.