Machine Learning
前往频道在 Telegram
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
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网络:📈 Telegram 频道 Machine Learning 的分析概览
频道 Machine Learning (@machinelearning9) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 40 427 名订阅者，在 技术与应用 类别中位列第 3 318，并在 叙利亚 地区排名第 225 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 40 427 名订阅者。
根据 14 七月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 413，过去 24 小时变化为 5，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 2.90%。内容发布后 24 小时内通常能获得 1.57% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 172 次浏览，首日通常累积 636 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 4。
- 主题关注点： 内容集中在 distance, insidead, gpu, learning, degree 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
凭借高频更新（最新数据采集于 15 七月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
40 427
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+524 小时
+1387 天
+41330 天
数据加载中...
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| 日期 | 订阅者增长 | 提及 | 频道 | |
| 15 七月 | +24 | |||
| 14 七月 | +9 | |||
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| 12 七月 | +28 | |||
| 11 七月 | +17 | |||
| 10 七月 | +27 | |||
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| 08 七月 | +12 | |||
| 07 七月 | +21 | |||
| 06 七月 | +10 | |||
| 05 七月 | +28 | |||
| 04 七月 | +22 | |||
| 03 七月 | +20 | |||
| 02 七月 | +18 | |||
| 01 七月 | +25 |
频道帖子
Repost from Machine Learning with Python
Hugging Face Viewer is now at 2300 viewable models! 😊 Would love more feedback and ideas!
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| 9 | 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.
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| 11 |  Reinforcement Learning Methods and Tutorials 🧠📚
In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.
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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. 📖✨
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