Machine Learning Site

25/12/2024

Maybe Santa himself is a part-time programmer. Merry Christmas everyone! 😊 🎄 🎁

24/12/2024

Curious how to make the most of Support Vector Machines in machine learning? This blog dives into three must-try examples—Which example will you start with?

Curious how to make the most of Support Vector Machines in machine learning? This blog dives into three must-try examples.

24/12/2024

Why not both? 😉

23/12/2024

Even I can build ML models in Assembly...
if I am allowed to use ChatGPT.

23/12/2024

Outliers ruining your ML pipeline? 🚨 Let’s fix that!

Detecting outliers is key to building reliable models. Use visualizations like boxplots, statistical methods like Z-scores, or even advanced tools like Isolation Forests to catch those sneaky anomalies.

✨ Pro Tip: Always analyze how outliers impact your data before deciding whether to remove or transform them!

💬 What’s your favorite method for detecting outliers? Let’s discuss below!👇

13/12/2024

To avoid overfitting or underfitting, it’s crucial to tune the C parameter wisely. If you’re struggling with outliers, tweaking the C parameter in your SVM model can make a significant difference.

😃 Want to know more about SVMs? Visit my blog for detailed examples with step by step guide on SVM and tutorial code. Link in bio 🔗.

12/12/2024

Real-world data isn't always straightforward!

🌎 📊 Most datasets out there are packed with complex, non-linear relationships that linear models simply can't handle. Think about patterns with curved boundaries or intricate feature interactions—ignoring these can lead to underfitting, here your model misses the mark entirely.

So, how do you tackle this? 🔧
- Kernel methods(like SVM with Polynomial or RBF kernels)
- Tree-based models (Random Forest, XGBoost)
- Neural networks (the champions of capturing non-linearity)

These techniques help uncover the hidden structure in your data, improving predictions and driving better decisions.

Among these, the Polynomial Kernel in SVM is a technique that transforms the input data into a higher-dimensional space, enabling SVM to create non-linear decision boundaries.

💬 What’s your go-to method for handling non-linear datasets? Let’s discuss below! 👇

24/11/2024

💡 Did you know SVMs are highly sensitive to feature scaling? Unscaled features can distort the decision boundary, leading to suboptimal performance. Always scale your data for the best results.

Why scale?
-SVMs rely on distance metrics—unscaled features can skew these distances.

How to scale?
- Use tools like StandardScaler or MinMaxScaler in sklearn.

Always fit the scaler on training data, then apply it to both training and test sets.

Now it's your turn! How do you usually preprocess your data for SVMs?

📢 Share your favorite tips, tools, or tricks in the comments. Let’s learn from each other and grow together as a community!

📍 Save this post if you found it helpful and share your experiences below.

23/11/2024

Understand how K Neighbors classifier work with this hands on exercise using MNIST data. Have a look 😊:

Practice an MNIST project using KNeighborsClassifier to classify handwritten digits. Perform hyperparameter tuning and enhance model performance with data augmentation techniques.

23/11/2024

I also like to train models! 🙂

22/11/2024

Hard margin SVM works great for perfectly separable data, but what happens when noise or overlap gets in the way? That’s where soft margin SVM steps up, finding the balance between flexibility and accuracy. This post provides more insight on the two SVM types with coding example.

Have you ever tackled this trade-off in your projects? Share your experience or thoughts in the comments—let’s spark a conversation! 😊

But wait, there's more. The latest blog contains an entire coding example on this topic with model analysis. Why not head directly over there and gain the entire information? 😉 Go ahead, link is in the bio 🔗.

18/11/2024

If you’ve been using Logistic Regression for classification, it’s time to explore SVMs—a game-changing algorithm in machine learning. SVMs focus on finding an optimal hyperplane that maximizes the margin between different classes, making them robust for classification tasks.

In my latest blog, I dive into:
- What SVMs are and how they work
- Hard vs. Soft Margin classifiers and when to use each
- A hands-on exercise with the Iris dataset, including stratified splitting for balanced training

👨‍💻 Code Highlight:

Visualize class overlap with scatterplots 📊
Train an SVM using Scikit-learn
See how the penalty term C adjusts your model’s flexibility—lower C generalizes better, while higher C is stricter!

📢 Follow for more tutorials, insights, and memes on ML and programming.

18/11/2024

Support Vector Machines (SVMs) are powerful supervised learning models known for their versatility and robustness. The idea of maximizing the margin between classes just makes so much sense for avoiding overfitting, even with smaller datasets.

What really impressed me, though, was the kernel trick—it’s like SVM’s secret weapon for handling non-linear data by mapping it into higher dimensions.

Plus, the fact that they work so well in high-dimensional spaces makes them perfect for tasks like text classification or image recognition. It’s one of those models that just feels so reliable once you get the hang of it!

Interested more? Check out my latest blog on SVMs where you'll find hands-on exercise to understand the machine learning classifier. Link in bio. 🔗

17/11/2024

don’t have to be complicated! Check out this simple guide with easy examples and practical tips to get you started.

Support Vector Machines don’t have to be complicated. Check out this simple guide with easy examples and practical tips to get you started.

12/11/2024

Support Vector Machines or SVMs are effective for complex, high-dimensional datasets and is less prone to overfitting in well-separated data. They’re versatile, working well for both binary and multi-class problems by using techniques like the "one-vs-all" approach.

SVMs also have robust kernel functions that enable them to handle non-linear data by mapping it into a higher-dimensional space for linear separation. These strengths make SVMs suitable for complex, real-world problems where accurate classification is essential.

Follow for more posts on programming and machine learning. 😊

🔗 Make sure to visit the website for tutorial code and other interesting blogs. Link in bio.

11/11/2024

DDS is the backbone of communication in ROS2, providing efficient, reliable, and scalable data exchange. It brings significant improvements over ROS1 by enabling real-time, decentralized, and fault-tolerant communication. This makes ROS2 suitable for a wide range of robotics applications, from autonomous vehicles to distributed multi-robot systems.

Below are the common DDS Implementations in ROS2:
🔸Fast RTPS: This is the default DDS implementation for ROS2. It is lightweight, open-source, and works well for many robotics applications.

🔸Cyclone DDS: An alternative DDS implementation that focuses on performance and scalability, particularly for edge devices.

🔸RTI Connext DDS: A commercial DDS implementation that provides advanced features and supports large-scale, mission-critical systems. Often used in industrial and automotive applications.

Stay tuned for more posts on ROS2- the framework for industrial robotic applications.

Follow for more content on programming and machine learning.

10/11/2024

He could've just done any programming without ChatGPT at all. That alone could've done the job.

09/11/2024

ROS2 Timer Callbacks: The Backbone of Autonomous Vehicle Efficiency!

In an autonomous vehicle, every millisecond counts. ROS2 timer callbacks allow different systems—like sensors and controls—to run at their own optimal intervals without interrupting each other.

From processing LIDAR data to updating steering commands in real-time, timers ensure each task stays in sync, enabling smooth, responsive navigation. Timer callbacks are a simple yet powerful way to keep complex tasks running harmoniously, making safe and intelligent driving possible.

Have you used ROS framework in your projects? Mention below in comments.