Machine Learning with Python

#Google #Colab notebooks to learn #Transformers 😎

Link: https://github.com/nlp-with-transformers/notebooks

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Pen and Paper Exercises in MachineLearning

Free 211-page PDF: arxiv.org/abs/2206.13446

GitHub: https://github.com/michaelgutmann/ml-pen-and-paper-exercises

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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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🗂 10 “Real Data Science Portfolio” Examples

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1️⃣ Ken Jee Portfolio | Data Scientist
▶️ Field: Sports data analysis
👤 Link: Portfolio

2️⃣ Yassine Alouini's Portfolio | Kegel Master
▶️ Domain: Machine Learning and Kegel Competitions
👤 Link: Portfolio

3️⃣ Tatman Portfolio | Data Scientist
▶️ Domain: Natural Language Processing (NLP)
👤 Link: Portfolio

4️⃣ Robinson Portfolio | Data Scientist
▶️ Field: Statistical analysis and R programming
👤 Link: Portfolio

5️⃣ Siraj Raval's Portfolio | AI Instructor
▶️ Field: Machine Learning and Artificial Intelligence
👤 Link: Portfolio

6️⃣ Julia Silge's Portfolio | Data Scientist
▶️ Domain: Organized data and data visualization
👤 Link: Portfolio

7️⃣ Mueller Portfolio | Developer Scikit-Learn
▶️ Field: Machine learning and open source projects
👤 Link: Portfolio

8️⃣ Wickham Portfolio | Data Scientist
▶️ Area: R programming and data visualization
👤 Link: Portfolio

9️⃣ Portfolio of François Puget | Kegel Master
▶️ Domain: Advanced Machine Learning Techniques
👤 Link: Portfolio

🔟 Emily's Portfolio | Data Analyst at Disney
▶️ Domain: Data visualization and storytelling
👤 Link: Portfolio

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Find your location on Map using Python

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🖥 190 Python projects

Link: https://amankharwal.medium.com/130-python-projects-with-source-code-61f498591bb

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1⃣ Dive into Deep Learning: https://d2l.ai

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Understanding Probability Distributions for Machine Learning with Python

In machine learning, probability distributions play a fundamental role for various reasons: modeling uncertainty of information and #data, applying optimization processes with stochastic settings, and performing inference processes, to name a few. Therefore, understanding the role and uses of probability distributions in machine learning is essential for designing robust machine learning models, choosing the right #algorithms, and interpreting outputs of a probabilistic nature, especially when building #models with #machinelearning-friendly programming languages like #Python.

This article unveils key #probability distributions relevant to machine learning, explores their applications in different machine learning tasks, and provides practical Python implementations to help practitioners apply these concepts effectively. A basic knowledge of the most common probability distributions is recommended to make the most of this reading.

Read Free: https://machinelearningmastery.com/understanding-probability-distributions-machine-learning-python/

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Generative AI for beginners by Microsoft

21 Lessons teaching everything you need to know to start building Generative AI applications

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The Hundred-Page Language Models Book

Read it:
https://github.com/aburkov/theLMbook

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pandas for Data Science - Guide

In this learning path, you’ll get started with pandas and get to know the ins and outs of how you can use it to analyze data with Python.

pandas is a game-changer for #datascience and analytics, particularly if you came to #Python because you were searching for something more powerful than #Excel and #VBA. #pandas uses fast, flexible, and expressive data structures designed to make working with relational or labeled data both easy and intuitive.

Read: https://realpython.com/learning-paths/pandas-data-science/

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The Large Language Model Course

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✔️ Awesome AI/ML Resources: Learn AI/ML for beginners with a roadmap and free resources.

🖥  https://github.com/armankhondker/awesome-ai-ml-resources

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