Check out the list of top 10 Python projects on GitHub given below.
1. Magenta: Explore the artist inside you with this python project. A Google Brain’s brainchild, it leverages deep learning and reinforcement learning algorithms to create drawings, music, and other similar artistic products.
2. Photon: Designing web crawlers can be fun with the Photon project. It is a fast crawler designed for open-source intelligence tools. Photon project helps you perform data crawling functions, which include extracting data from URLs, e-mails, social media accounts, XML and pdf files, and Amazon buckets.
3. Mail Pile: Want to learn some encrypting tricks? This project on GitHub can help you learn to send and receive PGP encrypted electronic mails. Powered by Bayesian classifiers, it is capable of automatic tagging and handling huge volumes of email data, all organized in a clean web interface.
4. XS Strike: XS Strike helps you design a vulnerability to check your network’s security. It is a security suite developed to detect vulnerability attacks. XSS attacks inject malicious scripts into web pages. XSS’s features include four handwritten parsers, a payload generator, a fuzzing engine, and a fast crawler.
5. Google Images Download: It is a script that looks for keywords and phrases to optionally download the image files. All you need to do is, replicate the source code of this project to get a sense of how it works in practice.
6. Pandas Project: Pandas library is a collection of data structures that can be used for flexible data analysis and data manipulation. Compared to other libraries, its flexibility, intuitiveness, and automated data manipulation processes make it a better choice for data manipulation.
7. Xonsh: Used for designing interactive applications without the need for command-line interpreters like Unix. It is a Python-powered Shell language that commands promptly. An easily scriptable application that comes with a standard library, and various types of variables and has its own virtual environment management system.
8. Manim: The Mathematical Animation Engine, Manim, can create video explainers. Using Python 3.7, it produces animated videos, with added illustrations and display graphs. Its source code is freely available on GitHub and for tutorials and installation guides, you can refer to their 3Blue1Brown YouTube channel.
9. AI Basketball Analysis: It is an artificial intelligence application that analyses basketball shots using an object detection concept. All you need to do is upload the files or submit them as a post requests to the API. Then the OpenPose library carries out the calculations to generate the results.
10. Rebound: A great project to put Python to use in building Stackoverflow content, this tool is built on the Urwid console user interface, and solves compiler errors. Using this tool, you can learn how the Beautiful Soup package scrapes StackOverflow and how subprocesses work to find compiler errors.
1. Magenta: Explore the artist inside you with this python project. A Google Brain’s brainchild, it leverages deep learning and reinforcement learning algorithms to create drawings, music, and other similar artistic products.
2. Photon: Designing web crawlers can be fun with the Photon project. It is a fast crawler designed for open-source intelligence tools. Photon project helps you perform data crawling functions, which include extracting data from URLs, e-mails, social media accounts, XML and pdf files, and Amazon buckets.
3. Mail Pile: Want to learn some encrypting tricks? This project on GitHub can help you learn to send and receive PGP encrypted electronic mails. Powered by Bayesian classifiers, it is capable of automatic tagging and handling huge volumes of email data, all organized in a clean web interface.
4. XS Strike: XS Strike helps you design a vulnerability to check your network’s security. It is a security suite developed to detect vulnerability attacks. XSS attacks inject malicious scripts into web pages. XSS’s features include four handwritten parsers, a payload generator, a fuzzing engine, and a fast crawler.
5. Google Images Download: It is a script that looks for keywords and phrases to optionally download the image files. All you need to do is, replicate the source code of this project to get a sense of how it works in practice.
6. Pandas Project: Pandas library is a collection of data structures that can be used for flexible data analysis and data manipulation. Compared to other libraries, its flexibility, intuitiveness, and automated data manipulation processes make it a better choice for data manipulation.
7. Xonsh: Used for designing interactive applications without the need for command-line interpreters like Unix. It is a Python-powered Shell language that commands promptly. An easily scriptable application that comes with a standard library, and various types of variables and has its own virtual environment management system.
8. Manim: The Mathematical Animation Engine, Manim, can create video explainers. Using Python 3.7, it produces animated videos, with added illustrations and display graphs. Its source code is freely available on GitHub and for tutorials and installation guides, you can refer to their 3Blue1Brown YouTube channel.
9. AI Basketball Analysis: It is an artificial intelligence application that analyses basketball shots using an object detection concept. All you need to do is upload the files or submit them as a post requests to the API. Then the OpenPose library carries out the calculations to generate the results.
10. Rebound: A great project to put Python to use in building Stackoverflow content, this tool is built on the Urwid console user interface, and solves compiler errors. Using this tool, you can learn how the Beautiful Soup package scrapes StackOverflow and how subprocesses work to find compiler errors.
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𝐒𝐢𝐦𝐩𝐥𝐞 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 😃
🙄 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠?
Imagine you're teaching a child to recognize fruits. You show them an apple, tell them it’s an apple, and next time they know it. That’s what Machine Learning does! But instead of a child, it’s a computer, and instead of fruits, it learns from data.
Machine Learning is about teaching computers to learn from past data so they can make smart decisions or predictions on their own, improving over time without needing new instructions.
🤔 𝐖𝐡𝐲 𝐢𝐬 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬?
Machine Learning makes data analytics super powerful. Instead of just looking at past data, it can help predict future trends, find patterns we didn’t notice, and make decisions that help businesses grow!
😮 𝐇𝐨𝐰 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬?
✅ 𝐋𝐞𝐚𝐫𝐧 𝐏𝐲𝐭𝐡𝐨𝐧: Python is the most commonly used language in ML. Start by getting comfortable with basic Python, then move on to ML-specific libraries like:
𝐩𝐚𝐧𝐝𝐚𝐬: For data manipulation.
𝐍𝐮𝐦𝐏𝐲: For numerical calculations.
𝐬𝐜𝐢𝐤𝐢𝐭-𝐥𝐞𝐚𝐫𝐧: For implementing basic ML algorithms.
✅ 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬 𝐨𝐟 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬: ML relies heavily on concepts like probability, distributions, and hypothesis testing. Understanding basic statistics will help you grasp how models work.
✅ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐨𝐧 𝐑𝐞𝐚𝐥 𝐃𝐚𝐭𝐚𝐬𝐞𝐭𝐬: Platforms like Kaggle offer datasets and ML competitions. Start by analyzing small datasets to understand how machine learning models make predictions.
✅ 𝐋𝐞𝐚𝐫𝐧 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Use tools like Matplotlib or Seaborn to visualize data. This will help you understand patterns in the data and how machine learning models interpret them.
✅ 𝐖𝐨𝐫𝐤 𝐨𝐧 𝐒𝐢𝐦𝐩𝐥𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Start with basic ML projects such as:
-Predicting house prices.
-Classifying emails as spam or not spam.
-Clustering customers based on their purchasing habits.
I have curated the best interview resources to crack Data Science Interviews
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🙄 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠?
Imagine you're teaching a child to recognize fruits. You show them an apple, tell them it’s an apple, and next time they know it. That’s what Machine Learning does! But instead of a child, it’s a computer, and instead of fruits, it learns from data.
Machine Learning is about teaching computers to learn from past data so they can make smart decisions or predictions on their own, improving over time without needing new instructions.
🤔 𝐖𝐡𝐲 𝐢𝐬 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬?
Machine Learning makes data analytics super powerful. Instead of just looking at past data, it can help predict future trends, find patterns we didn’t notice, and make decisions that help businesses grow!
😮 𝐇𝐨𝐰 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬?
✅ 𝐋𝐞𝐚𝐫𝐧 𝐏𝐲𝐭𝐡𝐨𝐧: Python is the most commonly used language in ML. Start by getting comfortable with basic Python, then move on to ML-specific libraries like:
𝐩𝐚𝐧𝐝𝐚𝐬: For data manipulation.
𝐍𝐮𝐦𝐏𝐲: For numerical calculations.
𝐬𝐜𝐢𝐤𝐢𝐭-𝐥𝐞𝐚𝐫𝐧: For implementing basic ML algorithms.
✅ 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬 𝐨𝐟 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬: ML relies heavily on concepts like probability, distributions, and hypothesis testing. Understanding basic statistics will help you grasp how models work.
✅ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐨𝐧 𝐑𝐞𝐚𝐥 𝐃𝐚𝐭𝐚𝐬𝐞𝐭𝐬: Platforms like Kaggle offer datasets and ML competitions. Start by analyzing small datasets to understand how machine learning models make predictions.
✅ 𝐋𝐞𝐚𝐫𝐧 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Use tools like Matplotlib or Seaborn to visualize data. This will help you understand patterns in the data and how machine learning models interpret them.
✅ 𝐖𝐨𝐫𝐤 𝐨𝐧 𝐒𝐢𝐦𝐩𝐥𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Start with basic ML projects such as:
-Predicting house prices.
-Classifying emails as spam or not spam.
-Clustering customers based on their purchasing habits.
I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like if you need similar content 😄👍
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5 Pandas Functions to Handle Missing Data
🔹 fillna() – Fill missing values with a specific value or method
🔹 interpolate() – Fill NaNs with interpolated values (e.g., linear, time-based)
🔹 ffill() – Forward-fill missing values with the previous valid entry
🔹 bfill() – Backward-fill missing values with the next valid entry
🔹 dropna() – Remove rows or columns with missing values
#Pandas
🔹 fillna() – Fill missing values with a specific value or method
🔹 interpolate() – Fill NaNs with interpolated values (e.g., linear, time-based)
🔹 ffill() – Forward-fill missing values with the previous valid entry
🔹 bfill() – Backward-fill missing values with the next valid entry
🔹 dropna() – Remove rows or columns with missing values
#Pandas
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