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Python Projects & Free Books

Python Projects & Free Books

前往频道在 Telegram

Python Interview Projects & Free Courses Admin: @Coderfun

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📈 Telegram 频道 Python Projects & Free Books 的分析概览

频道 Python Projects & Free Books (@pythonfreebootcamp) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 40 805 名订阅者,在 技术与应用 类别中位列第 3 179,并在 印度 地区排名第 9 475

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 40 805 名订阅者。

根据 27 八月, 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 -59,过去 24 小时变化为 -12,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 3.31%。内容发布后 24 小时内通常能获得 N/A% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 1 352 次浏览,首日通常累积 0 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 1
  • 主题关注点: 内容集中在 learning, analyst, framework, link:-, structure 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:
Python Interview Projects & Free Courses Admin: @Coderfun

凭借高频更新(最新数据采集于 28 八月, 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。

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40 805
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-1224 小时
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📈 Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide The process of building a stock price predicti
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📈 Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide The process of building a stock price prediction model using Python. 1. Import required modules 2. Obtaining historical data on stock prices 3. Selection of features. 4. Definition of features and target variable 5. Preparing data for training 6. Separation of data into training and test sets 7. Building and training the model 8. Making forecasts 9. Trading Strategy Testing

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Call for papers on AI to AI Journey* conference journal has started! Prize for the best scientific paper - 1 million roubles!
Call for papers on AI to AI Journey* conference journal has started! Prize for the best scientific paper - 1 million roubles! Selected papers will be published in the scientific journal Doklady Mathematics. 📖 The journal: •  Indexed in the largest bibliographic databases of scientific citations •  Accessible to an international audience and published in the world’s digital libraries Submit your article by August 20 and get the opportunity not only to publish your research the scientific journal, but also to present it at the AI Journey conference. Prize for the best article - 1 million roubles! More detailed information can be found in the Selection Rules -> AI Journey *AI Journey - a major online conference in the field of AI technologies

⌨️ Learn About Python List Methods
⌨️ Learn About Python List Methods

⌨️ Grammar Correction using Python
⌨️ Grammar Correction using Python

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Top 10 Data Libraries for Python
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Top 10 Data Libraries for Python

🔅 Convert Video to Audio using Python
🔅 Convert Video to Audio using Python

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Build AI Agents with Python ✅
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Build AI Agents with Python ✅

Repost from Generative AI
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𝗝𝗣 𝗠𝗼𝗿𝗴𝗮𝗻 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍 JPMorgan offers free virtual internships to help you develop industry-specific tech, finance, and research skills.  - Software Engineering Internship - Investment Banking Program - Quantitative Research Internship   𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/4gHGofl Enroll For FREE & Get Certified 🎓

To start with Machine Learning:    1. Learn Python    2. Practice using Google Colab     Take these free courses: https://t.me/datasciencefun/290 If you need a bit more time before diving deeper, finish the Kaggle tutorials. At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle. If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed. From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit. The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them: https://t.me/datasciencefree/259 Many different books will help you. The attached image will give you an idea of my favorite ones. Finally, keep these three ideas in mind: 1. Start by working on solved problems so you can find help whenever you get stuck. 2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice. 3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others. During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right. Here is the good news: Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space. Focus on finding your path, and Write. More. Code. That's how you win.✌️✌️

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10 Must-Know Python Libraries for LLMs in 2025 1. Hugging Face Transformers Best for: Pre-trained LLMs, fine-tuning, inference 2. LangChain Best for: LLM-powered apps, chatbots, AI agents 3. SpaCy Best for: Tokenization, named entity recognition (NER), dependency parsing 4. Natural Language Toolkit (NLTK) Best for: Linguistic analysis, tokenization, POS tagging 5. SentenceTransformers Best for: Semantic search, similarity, clustering 6. FastText Best for: Word embeddings, text classification 7. Gensim Best for: Word2Vec, topic modeling, document embeddings 8. Stanza Best for: Named entity recognition (NER), POS tagging 9. TextBlob Best for: Sentiment analysis, POS tagging, text processing 10. Polyglot Best for: Multi-language NLP, named entity recognition, word embeddings

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