Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.

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Python, Bash and SQL Essentials for Data Engineering Specialization

What you'll learn
Develop #dataengineering solutions with a minimal and essential subset of the Python language and the Linux environment

Design scripts to connect and query a #SQL #database using #Python

Use a #scraping library in Python to read, identify and extract data from websites

Enroll Free: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke

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pandas for Data Science - Guide

In this learning path, youโ€™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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Generative AI for beginners by Microsoft

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

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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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