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Machine Learning with Python
前往频道在 Telegram
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
显示更多📈 Telegram 频道 Machine Learning with Python 的分析概览
频道 Machine Learning with Python (@codeprogrammer) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 67 809 名订阅者，在 教育 类别中位列第 2 416，并在 印度 地区排名第 5 038 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 67 809 名订阅者。
根据 09 六月, 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 70，过去 24 小时变化为 10，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 2.94%。内容发布后 24 小时内通常能获得 2.44% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 997 次浏览，首日通常累积 1 652 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 7。
- 主题关注点： 内容集中在 insidead, learning, degree, evaluation, algorithm 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：
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
凭借高频更新（最新数据采集于 10 六月, 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 教育 类别中的关键影响点。
67 809
订阅者
+1024 小时
+127 天
+7030 天
帖子存档
python-docx: Create and Modify Word Documents #python
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
pip install python-docxfrom docx import Document
document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")
document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'
+2
Top 150 Python Interview Questions
This PDF covers the most frequently asked Python questions with answers to help you prepare for upcoming interviews.
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Repost from Machine Learning with Python
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Repost from Machine Learning with Python
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𝗣𝗵𝗮𝘀𝗲 𝟭: 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 (𝗪𝗲𝗲𝗸 𝟭-𝟮)
● Understand perceptrons, sigmoid, ReLU, tanh
● Learn cost functions, gradient descent, and derivatives
● Implement binary logistic regression using NumPy
𝗣𝗵𝗮𝘀𝗲 𝟮: 𝗦𝗵𝗮𝗹𝗹𝗼𝘄 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗪𝗲𝗲𝗸 𝟯-𝟰)
● Build a neural net with one hidden layer
● Compare activation functions (sigmoid vs tanh vs ReLU)
● Train your model to classify simple images
𝗣𝗵𝗮𝘀𝗲 𝟯: 𝗗𝗲𝗲𝗽 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗪𝗲𝗲𝗸 𝟱-𝟲)
● Forward and backward propagation through multiple layers
● Parameter initialization and tuning
● Implement L-layer neural networks from scratch
𝗣𝗵𝗮𝘀𝗲 𝟰: 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗥𝗲𝗴𝘂𝗹𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗪𝗲𝗲𝗸 𝟳-𝟴)
● Learn mini-batch gradient descent, RMSProp, and Adam
● Apply L2 and Dropout regularization to avoid overfitting
● Boost your model’s performance with better convergence
𝗣𝗵𝗮𝘀𝗲 𝟱: 𝗧𝗲𝗻𝘀𝗼𝗿𝗙𝗹𝗼𝘄 & 𝗥𝗲𝗮𝗹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 (𝗪𝗲𝗲𝗸 𝟵-𝟭𝟬)
● Build models using TensorFlow and Keras
● Normalize data, tune hyperparameters, and visualize metrics
● Create multi-class classifiers using softmax
𝗣𝗵𝗮𝘀𝗲 𝟲: 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 & 𝗖𝗮𝗿𝗲𝗲𝗿 𝗣𝗿𝗲𝗽 (𝗪𝗲𝗲𝗸 𝟭𝟭-𝟭𝟮)
● Work on image recognition, text classification, and real datasets
● Learn model deployment techniques
● Prepare for interviews with hands-on projects and GitHub repo
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