My favorite way to work with multiple filters in pandas.Series — not a chain of .loc, but a single mask. 🐼

The chain looks neat, but breaks on real data and easily gives unexpected results:

s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]

The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🤯

It's more reliable to gather everything into one expression:

s = pd.Series([10, 15, 20, 25, 30])

mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]

One mask, one point of truth. ✅

It's easier to debug. Fewer surprises when the code grows. 🚀

#Pandas #Python #DataScience #CodingTips #DataEngineering #Debugging

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500 AI/ML/Computer Vision/NLP projects with code 🚀

This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP 🧠

All examples come with code, so you can not just read them, but immediately analyze and run them ⚙️

➡️ Link to GitHub:
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

#AI #MachineLearning #DeepLearning #ComputerVision #NLP #DataScience

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Don't learn ML by randomly jumping through tutorials. 🚫📚

DS-ML Bootcamp is a public repository for a Data Science and machine learning course for beginners who want a structured path from zero to practical projects. 🚀📊

It helps transition from installation and concepts to practical ML work, organizing lessons, assignments, code examples, datasets, and solutions around the main machine learning workflow. 🛠️🧠

Key features:

- End-to-end workflow - covers data collection, preprocessing, train/test split, model selection, training, evaluation, and deployment 🔄📈
- Lesson-based structure - starts with tools/setup, Data Science, ML, data fundamentals, and regression 📚🧮
- Practical materials - assignments give learners structured tasks, not just reading notes ✍️✅
- Code + datasets - Python examples and raw CSV datasets included for exercises 🐍📂
- Set up for repetition - the README says you can clone the repository and use Jupyter or VS Code while going through lessons 💻🔁

Free public repository on GitHub. 🆓
https://github.com/goobolabs/ds-ml-bootcamp

#MachineLearning #DataScience #Coding #Python #AI #Learning

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The math.perm() method

The math.perm() method in Python returns the number of ways to select k elements from n elements, with and without repetition. 🧮

Syntax:

math.perm(n, k)

Where:
n: The number of elements from which k elements are selected.
k: The number of elements that are selected.

In the first example, the method returns the number of ways to select 3 elements from 5 elements. The result is 60 ways. 📊
In the second example, the method returns the number of ways to select 5 elements from 10 elements. The result is 252 ways. 🚀

#Python #Math #Coding #Programming #DataScience #Tech

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Cheat sheet for Scikit-learn: 📚 Scikit-learn is a Python library for machine learning.

📥 Loading Data - downloading and preparing data.
🧼 Preprocessing - standardization, normalization, and feature processing.
🏗️ Create Your Model - creating models for classification, regression, and clustering.
🎯 Model Fitting - training the model on data.
🔮 Prediction - obtaining forecasts.
📊 Evaluate Performance - assessing the quality of the model using various metrics.
🔄 Cross-Validation - checking the model on different samples.
⚙️ Tune Your Model - optimizing parameters using Grid Search and Randomized Search.

#ScikitLearn #MachineLearning #Python #DataScience #AI #MLOps

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🔖 The Legendary MIT Textbook on Mathematics for Computer Science

Mathematics for Computer Science is one of the best free textbooks for developers, ML engineers, and data scientists.

It contains over 1000 pages covering discrete mathematics, logic, graphs, probability, combinatorics, recurrence relations, and other fundamental topics.

⛓️ Link to the textbook:
https://people.csail.mit.edu/meyer/mcs.pdf

#ComputerScience #Mathematics #MachineLearning #DataScience #MIT #OpenSource

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Combining Plots in Matplotlib 📊

In Matplotlib, you can easily combine multiple plots in a single window using the subplot() function. Simply create the necessary plots, specify their layout, add titles, and you'll get a clear visualization for easy data comparison.

#Matplotlib #DataVisualization #Python #DataScience #Coding #Plotting

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Reinforcement Learning Methods and Tutorials 🧠📚

In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.

Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow 🚀

Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms. 📖✨

#ReinforcementLearning #MachineLearning #AI #DeepLearning #TechTutorials #DataScience

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