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Discover powerful insights with Python, Machine Learning, Coding, and Rβ€”your essential toolkit for data-driven solutions, smart alg

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πŸ—‚ 10 β€œReal Data Science Portfolio” Examples

πŸ“ I've brought you 10 of the best portfolios from data science professionals, each of whom has followed a unique path! Check out these 10 and get inspired to build a strong portfolio of your own!πŸ‘‡
1️⃣ Ken Jee Portfolio | Data Scientist
▢️ Field: Sports data analysis
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2️⃣ Yassine Alouini's Portfolio | Kegel Master
▢️ Domain: Machine Learning and Kegel Competitions
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3️⃣ Tatman Portfolio | Data Scientist
▢️ Domain: Natural Language Processing (NLP)
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4️⃣ Robinson Portfolio | Data Scientist
▢️ Field: Statistical analysis and R programming
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5️⃣ Siraj Raval's Portfolio | AI Instructor
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6️⃣ Julia Silge's Portfolio | Data Scientist
▢️ Domain: Organized data and data visualization
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7️⃣ Mueller Portfolio | Developer Scikit-Learn
▢️ Field: Machine learning and open source projects
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8️⃣ Wickham Portfolio | Data Scientist
▢️ Area: R programming and data visualization
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9️⃣ Portfolio of FranΓ§ois Puget | Kegel Master
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πŸ”Ÿ Emily's Portfolio | Data Analyst at Disney
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Generative vs. discriminative models in ML:

Generative models:
- learn the distribution so they can generate new samples.
- possess discriminative properties, we can use them for classification.

Discriminative models don't have generative properties.

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TensorFlow v2.0 Cheat Sheet

#TensorFlow is an open-source software library for highperformance numerical computation. Its flexible architecture enables to easily deploy computation across a variety of platforms (CPUs, GPUs, and TPUs), as well as mobile and edge devices, desktops, and clusters of servers. TensorFlow comes with strong support for machine learning and deep learning.


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https://t.me/CodeProgrammer
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πŸ”₯ MIT has updated its famous course 6.S191: Introduction to Deep Learning.

The program covers topics of #NLP, #CV, #LLM and the use of technology in medicine, offering a full cycle of training - from theory to practical classes using current versions of libraries.

The course is designed even for beginners: if you know how to take derivatives and multiply matrices, everything else will be explained in the process.

The lectures are released for free on YouTube and the #MIT platform on Mondays, with the first one already available

.

All slides, #code and additional materials can be found at the link provided.

πŸ“Œ Fresh lecture : https://youtu.be/alfdI7S6wCY?si=6682DD2LlFwmghew

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Important Methods in #Pandas Package

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Numpy @CodeProgrammer.pdf
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πŸ³οΈβ€πŸŒˆ "NumPy Library" Tutorial

πŸ‘¨πŸ»β€πŸ’» For the past few days, I've been busy preparing this comprehensive tutorial on the NumPy library for data science, trying to cover all the tips and tricks of this library.

βœ… Why is this booklet different? Because it is not written based on just theoretical concepts, but is the result of my own experiences and learning. It has real and practical examples that will help you better understand #NumPy concepts and use them in your projects.πŸ’―

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