Machine Learning

Hugging Face Viewer is now at 2300 viewable models! 😊 Would love more feedback and ideas! It's a free interactive graph visualizer for learning about the architectures of open source AI models! 🚀 Hovering nodes in the graph links to a definitions + animation and the paper that introduced it! 🌟 hfviewer.com #HuggingFace #AI #MachineLearning #OpenSource #TechNews #DataViz ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

The first bot in the world of Telegram that offers free coupons, free certificates, and job opportunities based on AI https://telegram.me/UdemySybot

Create your own AI assistant for free in 5 minutes. It's a familiar problem: everyone wants a personal AI assistant, but building one from scratch usually means servers, API keys, integrations, maintenance, and a ton of technical overhead. Amplify takes care of all of this for you. In about 5 minutes, you'll have a personal AI agent connected to your Google account—Gmail, Drive, Calendar, Docs, Slides, Sheets, and more. Google integration is officially verified. 🗣You can communicate with your assistant anywhere: Telegram, WhatsApp, Slack, WeChat, or Discord. It can help with email, draft replies to text or voice messages, send emails, set reminders, create and manage spreadsheets, generate images, create videos, edit short videos, work with PDFs, Notion, Obsidian, and much more. Dozens of skills are already available, and the list is constantly growing. If you need a custom skill for your workflow, business, or team, the Amplify team will quickly develop and implement it. The pricing is simple: $10 per month plus pay only for the features you actually use. No confusing token system—the cost of each action is clearly displayed in your dashboard. And if you already have a ChatGPT subscription, you can sign up and essentially avoid paying separately for the AI ​​model. 😎For subscribers: use the promo code and get two months free + $10 credit to your balance. After registering, you'll receive your own promo code. If someone else signs up with it, you'll get an extra month free. Try Amplify here: https://getamplify.team/ Promo code: CODEPROGRAMMER

Get a job opportunity and obtain free courses and certificates to increase your experience. http://t.me/UdemySybot?start

Feature Scaling: Why Feature Scaling Affects Model Training Feature scaling is often overlooked because it seems like just another data preprocessing step. However, in practice, it often helps models train faster and more stably. Imagine one feature has values ranging from 0 to 1, while another has values ranging from 0 to 10,000. Although both features may be equally important for prediction, it's more difficult for the optimizer to work with such data. This means it has to take more steps to find a good solution. Additionally, regularization becomes less effective because features with different scales require coefficients of different magnitudes. Let's look at how this looks in a simple example. Install dependencies:

pip install numpy scikit-learn

Import libraries:

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score

Let's create a small synthetic dataset. It will have two features: the first has a normal scale, and the second is about a thousand times larger. Importantly, both features actually influence the target variable. That is, the only difference between them is the scale.

np.random.seed(42)
x_small = np.random.normal(0, 1, 300)
x_large = np.random.normal(0, 1000, 300)

X = np.vstack([x_small, x_large]).T

y = (x_small + 0.001 * x_large > 0).astype(int)

Now, let's split the data into training and testing sets. We won't scale anything yet—first, let's see how the model behaves on the original data.

X_train, X_test, y_train, y_test = train_test_split(
    X, y,
    test_size=0.3,
    random_state=42,
    stratify=y
)

Let's train a logistic regression model without scaling. In addition to the model's quality, let's also look at the number of iterations (n_iter_). This metric shows how much work the optimizer had to do to find the coefficients.

model = LogisticRegression()
model.fit(X_train, y_train)

pred = model.predict_proba(X_test)[:, 1]

print("ROC-AUC:", roc_auc_score(y_test, pred))
print("Iterations:", model.n_iter_)

Now, let's scale the features to the same scale using StandardScaler. It calculates the mean and standard deviation only for the training set and then uses the same values for the test set. This is important because the model should not "peek" at the test data during training. After this transformation, both features are approximately on the same scale, and it becomes easier for the optimizer to work with them.

scaler = StandardScaler()

X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

Now, let's retrain the model. We're using the same model, the same data, and the same parameters. The only difference is that the features are now scaled.

model = LogisticRegression()
model.fit(X_train_scaled, y_train)

pred = model.predict_proba(X_test_scaled)[:, 1]

print("ROC-AUC (scaled):", roc_auc_score(y_test, pred))
print("Iterations (scaled):", model.n_iter_)

Most often, the ROC-AUC doesn't change much. However, the number of iterations becomes smaller. This means that the optimizer found a solution faster, and the training was more stable. 🔥 Feature scaling is a simple data preprocessing step that, in many cases, allows the model to train faster and more stably. For logistic regression, SVMs, neural networks, and other algorithms that use numerical optimization, it's best not to skip it. ✨ #DataScience #MachineLearning #Python #Coding #Tech #AI ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

The first bot in Telegram that offers free Udemy coupons https://t.me/UdemySybot

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 ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🚀 Looking for a portfolio-ready NLP project? I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset. You’ll see how far classical NLP can go with: 📝 Bag-of-Words and TF-IDF 🔤 Character n-grams 📊 Model comparison 🧩 Ensemble stacking It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step: 🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/

🔥 Free IT Cert Resources – Grab Them While They're Hot! 🌈SPOTO just dropped a bunch of 100% free study kits for 2026 – covering #Cisco, #AWS, #PMP, #AI, #Python, #Excel, and #Cybersecurity 💥No signup traps, no hidden fees – just click and download. 📘 FREE Cert E‑Book → https://bit.ly/4wkiLAT 🪜 Online FREE Course → https://bit.ly/4vHFJSz ☁️ FREE AI Materials → https://bit.ly/4wdu7X6 📊 Cloud Study Guide → https://bit.ly/4y0HyeW 🧠 Free Mock Exam → https://bit.ly/4ff8jos Tag a friend who's also on this journey – Get certified together! 💪 🌐 Join the community: https://chat.whatsapp.com/FmbIbbqm2QhKglVpVTSH4d/ 📲 Need personalized help? → https://wa.link/6k7042

Understanding Datasets 😉

Code smarter, not costlier. 🚀 Get powerful AI coding agents, seamless OpenAI-compatible APIs, and more value for every dollar. Build faster, automate more, and let AI work directly with your code. Join now and start creating without limits.

🔥 Free IT Cert Resources – Grab Them While They're Hot! 🌈SPOTO just dropped a bunch of 100% free study kits for 2026 – covering #Cisco, #AWS, #PMP, #AI, #Python, #Excel, and #Cybersecurity 💥No signup traps, no hidden fees – just click and download. 📘 FREE Cert E‑Book → https://bit.ly/4wkiLAT 🪜 Online FREE Course → https://bit.ly/4vHFJSz ☁️ FREE AI Materials → https://bit.ly/4wdu7X6 📊 Cloud Study Guide → https://bit.ly/4y0HyeW 🧠 Free Mock Exam → https://bit.ly/4ff8jos Tag a friend who's also on this journey – Get certified together! 💪 🌐 Join the community: https://chat.whatsapp.com/FmbIbbqm2QhKglVpVTSH4d/ 📲 Need personalized help? → https://wa.link/6k7042