Machine Learning with Python

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣  Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/codeprogrammer

🗂 10 “Real Data Science Portfolio” Examples

📁 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!👇

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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✅ Free ML Books You Should Know! 1⃣ Dive into Deep Learning: https://d2l.ai 🔢 Deep Learning: https://deeplearningbook.org 🔢 Interpretable Machine Learning: https://christophm.github.io/interpretable-ml-book/ 🔢 Machine Learning Yearning: https://info.deeplearning.ai/machine-learning-yearning-book 🔢 Mathematics for Machine Learning: https://mml-book.github.io 🔢 An Introduction to Statistical Learning: https://statlearning.com

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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 machine learning-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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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/ https://t.me/CodeProgrammer

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