Ninja Coding

Hey Ninja Coders! 🥷 Let’s continue leveling up our Codejitsu with unsupervised machine learning! In this episode, I’m walking through a simple k-means clustering algorithm built from scratch with numpy and pandas. Give it a go, and as always, thanks for your amazing support! https://youtu.be/yWujiiSvlcs?si=mn2lzmdC7Nums50q

Hey Ninja Coders 🥷 I’ve taken the time to re-upload some of my episodes with an improved voiceover for a better experience. The latest one focuses on Image Clustering with the K-means Algorithm—a topic that’s both insightful and practical. I’d truly appreciate it if you could support my work by giving it a watch and leaving a like. Your feedback means a lot to me and helps me continue creating better content for you. You can watch it here: https://youtu.be/6Z6QOYpGh9M?feature=shared

Happy New Year, everyone! 🎉 I wish you all the very best in 2025! May this year bring you plenty of joy, growth, and exciting programming moments. If you’re looking for something fun to do in your free time, I just released a new episode on my YouTube channel! You can check it out here https://youtu.be/8zfDNgOGwTY?si=mAn6H1BjCGn_kltm I think that the work with images is a fantastic way to dive into linear algebra and get comfortable with matrices. It's a super hands-on way to learn, and I think you’ll enjoy it! Hope you find it helpful and inspiring! Let’s make 2025 a year full of learning and coding adventures!

Hey friends! 👋 I’ve been tied up with other projects and haven’t had time for full data analysis videos lately. But, I’m excited to share some shorter videos focusing on data visualization with Python in the meantime. Your support really helps me grow! Here’s my newest video—hope you find it valuable! https://youtu.be/XJ-vHd9TsyE?si=E1e-sTgeo8ExS-qd

🚀 Ready to learn data science on the go? Install JupyterLab in Termux on your Android device! 📱 Quick Setup: 0. Install Termux apk: choose from here 1. Update Termux: pkg update && pkg upgrade -y 2. Install essentials: pkg install -y build-essential libzmq rust python 3. Get Jupyter: pip install jupyter 4. For Python 3.11, run: pkg install -y patchelf patchelf --add-needed libpython3.11.so $PREFIX/lib/python3.11/site-packages/zmq/backend/cython/_zmq.cpython-311.so Launch JupyterLab:

jupyter-lab

Paste the URL in your browser and start coding! 📱💡 Warning: Installation steps may change in future updates. Stay agile! ⚠️

🐋 Big win in my dev world! After endlessly reconfiguring RStudio and packages on every machine, I finally dove into Docker and crafted my own custom image. Now, I can launch my tailored dev environment on ANY PC with zero hassle! But that's not all—Docker isn't just for R; it's a game-changer for any language, including Python for data science! Say goodbye to repetitive setups and hello to smooth, versatile coding!

Hey everyone! I haven’t had a chance to post lately because I’m busy revising the materials for my upcoming course on Introduction to Python Programming. Currently, I’m working on adding a section about regular expressions. You can check out the current version https://vdsukhov.github.io/python-intro-course/chapters/regex.html (still a work in progress). I’d love to hear how you prefer to learn regular expressions—drop your favorite methods or resources in the comments

Hey Ninjas 🥷! Let's talk a bit about Linear Regression. Here is the deck about it #Data #DataScience #ML #MachineLearning

Logistic Regression 0️⃣ or 1️⃣ #Data #DataScience #ML #MachineLearning

🚀 I've just released a new YouTube video on "Image Processing with Python: Color Transfer"! I know that you more interested in Machine Learning and Data Science, but I'd love your support in watching, liking, and sharing the video with anyone who might find it helpful. Your feedback and engagement mean the world to me! Watch it here: https://youtu.be/_kbkJPGcEjE?si=6hyvmsT5Ud9RxaDi Thank you so much for your support! 🙌

Supervised Learning Hello there! Today, we're going to explore a brief introduction to Supervised Learning. I'm curious, what's your favorite machine learning approach? I'd love to hear about it in the comments! #Data #DataScience #ML #MachineLearning

Data Cleaning Hey there 👋! Here are bite-sized cards about data cleaning in data science #Data #DataScience #ML #MachineLearning #Python

Exploratory Data Analysis Hello! Here are some bite-sized cards about another topic from the field of data science. #Data #DataScience #ML #MachineLearning #Python

Python Environment for Data Science #Data #DataScience #ML #MachineLearning #Python

Machine Learning Tasks #Data #DataScience #ML #MachineLearning #Python

Hello there! Let's embark on a fun journey through various topics related to data science and machine learning. I'll be sharing bite-sized cards full of interesting summaries. Go ahead and drop a comment if you're curious about any specific topic, I'd be more than happy to delve deeper into it with you. #Data #DataScience #ML #MachineLearning #Python #programming

Hey everyone! Check out my latest episode where I dive into the fascinating world of image processing with K-means clustering and LAB color space. This video not only helps you extract beautiful color palettes from images but also gives you a visual understanding of the key concepts behind various machine learning methods. Perfect for both beginners and seasoned data enthusiasts! Don't miss out – grab your coffee, get comfy, and let’s explore the magic of data science together! #MachineLearning #DataScience #ImageProcessing #ML https://youtu.be/487SRMlCIyU

Hey Ninjas 🥷! I'm currently working on a new episode about image processing. I'll be discussing clustering, a crucial task in machine learning. Specifically, I'll show you how to apply K-means clustering in the context of images. Stay tuned! I'll share the video link as soon as I can. In the meantime, you can check out the text version of the upcoming video here: https://svitnote.notion.site/Image-Processing-003-Color-Segmentation-with-K-means-Clustering-b384691826b44b60ab9ba0103b00cff7?pvs=4

Hey everyone, Exciting news! I recently delved into the fascinating world of Ollama and large language models on my laptop, and I'm absolutely hooked! So much so that I've put together a short video guide on how to install these game-changers on Linux-like systems. Here's the kicker: I've also included a couple of practical examples demonstrating how these models can turbocharge your programming skills! Check it out and let me know what you think. Your support means everything to me! https://youtu.be/0K6uiiLrLic?si=R1amD9zBT4LQ7xIc

Unlock the power of GPT right on your own computer! Guess what? You can actually run all sorts of powerful language models straight from your terminal. And the best part? You don't need any fancy setups or complicated installs. All you need is ollama, which works on macOS, Linux, and Windows. Here's how simple it is to get started on Linux: 1. Open your terminal. 2. Copy and paste this line:

curl -fsSL <https://ollama.com/install.sh> | sh

1. Hit Enter. Boom! You're all set up! Now, let's try it out. You can run different language models right from your terminal. For example, let's start with llama3, a model created by Meta Inc. Just type this command:

ollama run llama3

And that's it! You're ready to go. Check out the screenshot attached to see how I asked it to write a simple bubble sort algorithm in Python. Cool, right?