500 AI/ML/Computer Vision/NLP projects with code ๐
This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP ๐ง
All examples come with code, so you can not just read them, but immediately analyze and run them โ๏ธ
โก๏ธ 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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This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP ๐ง
All examples come with code, so you can not just read them, but immediately analyze and run them โ๏ธ
โก๏ธ 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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โค4
Don't learn ML by randomly jumping through tutorials. ๐ซ๐
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. ๐๐
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. ๐ ๏ธ๐ง
Key features:
- End-to-end workflow - covers data collection, preprocessing, train/test split, model selection, training, evaluation, and deployment ๐๐
- Lesson-based structure - starts with tools/setup, Data Science, ML, data fundamentals, and regression ๐๐งฎ
- Practical materials - assignments give learners structured tasks, not just reading notes โ๏ธโ
- Code + datasets - Python examples and raw CSV datasets included for exercises ๐๐
- Set up for repetition - the README says you can clone the repository and use Jupyter or VS Code while going through lessons ๐ป๐
Free public repository on GitHub. ๐
https://github.com/goobolabs/ds-ml-bootcamp
#MachineLearning #DataScience #Coding #Python #AI #Learning
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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. ๐๐
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. ๐ ๏ธ๐ง
Key features:
- End-to-end workflow - covers data collection, preprocessing, train/test split, model selection, training, evaluation, and deployment ๐๐
- Lesson-based structure - starts with tools/setup, Data Science, ML, data fundamentals, and regression ๐๐งฎ
- Practical materials - assignments give learners structured tasks, not just reading notes โ๏ธโ
- Code + datasets - Python examples and raw CSV datasets included for exercises ๐๐
- Set up for repetition - the README says you can clone the repository and use Jupyter or VS Code while going through lessons ๐ป๐
Free public repository on GitHub. ๐
https://github.com/goobolabs/ds-ml-bootcamp
#MachineLearning #DataScience #Coding #Python #AI #Learning
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GitHub
GitHub - goobolabs/Jun-ds-ml-bootcamp-2026: Data Science and Machine Learning Bootcamp. (Jun - 2026)
Data Science and Machine Learning Bootcamp. (Jun - 2026) - goobolabs/Jun-ds-ml-bootcamp-2026
โค7
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. ๐งฎ
Syntax:
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. ๐
In the second example, the method returns the number of ways to select 5 elements from 10 elements. The result is 252 ways. ๐
#Python #Math #Coding #Programming #DataScience #Tech
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The math.perm() method in Python returns the number of ways to select k elements from n elements, with and without repetition. ๐งฎ
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. ๐
In the second example, the method returns the number of ways to select 5 elements from 10 elements. The result is 252 ways. ๐
#Python #Math #Coding #Programming #DataScience #Tech
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โค10
Cheat sheet for Scikit-learn: ๐ Scikit-learn is a Python library for machine learning.
๐ฅ Loading Data - downloading and preparing data.
๐งผ Preprocessing - standardization, normalization, and feature processing.
๐๏ธ Create Your Model - creating models for classification, regression, and clustering.
๐ฏ Model Fitting - training the model on data.
๐ฎ Prediction - obtaining forecasts.
๐ Evaluate Performance - assessing the quality of the model using various metrics.
๐ Cross-Validation - checking the model on different samples.
โ๏ธ Tune Your Model - optimizing parameters using Grid Search and Randomized Search.
#ScikitLearn #MachineLearning #Python #DataScience #AI #MLOps
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๐ฅ Loading Data - downloading and preparing data.
๐งผ Preprocessing - standardization, normalization, and feature processing.
๐๏ธ Create Your Model - creating models for classification, regression, and clustering.
๐ฏ Model Fitting - training the model on data.
๐ฎ Prediction - obtaining forecasts.
๐ Evaluate Performance - assessing the quality of the model using various metrics.
๐ Cross-Validation - checking the model on different samples.
โ๏ธ Tune Your Model - optimizing parameters using Grid Search and Randomized Search.
#ScikitLearn #MachineLearning #Python #DataScience #AI #MLOps
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โค6๐2