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Coding Free Books | Python | AI

Coding Free Books | Python | AI

Kanalga Telegramโ€™da oโ€˜tish

Best Channel for Programmers and Hackers All in one channel to learn ๐Ÿ‘‡ 1. Python 2. Ethical Hacking 3. Java 4. App development 5. Machine learning 6. Data structures 7. Algorithms Promotions: @coderfun

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๐Ÿ“ˆ Telegram kanali Coding Free Books | Python | AI analitikasi

Coding Free Books | Python | AI (@codingwithsagar) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 30 887 obunachidan iborat bo'lib, Taสผlim toifasida 6 255-o'rinni va Hindiston mintaqasida 13 646-o'rinni egallagan.

๐Ÿ“Š Auditoriya koโ€˜rsatkichlari va dinamika

ะฝะตะฒั–ะดะพะผะพ sanasidan buyon loyiha tez oโ€˜sib, 30 887 obunachiga ega boโ€˜ldi.

20 Iyun, 2026 dagi oxirgi maโ€™lumotlarga koโ€˜ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 155 ga, soโ€˜nggi 24 soatda esa -7 ga oโ€˜zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya oโ€˜rtacha 3.69% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining N/A% ini tashkil etuvchi reaksiyalarni toโ€˜playdi.
  • Post qamrovi: Har bir post oโ€˜rtacha 1 139 marta koโ€˜riladi; birinchi sutkada odatda 0 ta koโ€˜rish yigโ€˜iladi.
  • Reaksiyalar va oโ€˜zaro taโ€™sir: Auditoriya faol: har bir postga oโ€˜rtacha 4 ta reaksiya keladi.
  • Tematik yoโ€˜nalishlar: Kontent learning, link:-, css, algorithm, sql kabi asosiy mavzularga jamlangan.

๐Ÿ“ Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโ€™riflaydi:
โ€œBest Channel for Programmers and Hackers All in one channel to learn ๐Ÿ‘‡ 1. Python 2. Ethical Hacking 3. Java 4. App development 5. Machine learning 6. Data structures 7. Algorithms Promotions: @coderfunโ€

Yuqori yangilanish chastotasi (oxirgi maโ€™lumot 21 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโ€˜lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taสผlim toifasidagi muhim taโ€™sir nuqtasiga aylantirishini koโ€˜rsatadi.

30 887
Obunachilar
-724 soatlar
-27 kunlar
+15530 kunlar
Postlar arxiv
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Linux_ The_Ultimate_Guide.pdf4.72 MB

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Dynamic Programming Was Really Hard for me, Until I found This. 1. DP for Beginners [Problems | Patterns | Sample Solutions] - https://lnkd.in/d5b9uJn6 2. DP Patterns - https://lnkd.in/dJPz8Dvn 3. Knapsack problems - https://lnkd.in/dE_rg6dd 4. How to solve DP-String? Template and 4 Steps to be followed - https://lnkd.in/dqhu3MZf 5. Dynamic Programming Questions thread - https://lnkd.in/d-pVR4rg 6. How to approach DP problems - https://lnkd.in/dwbh-XqJ 7. Iterative DP for subset sum problems - https://lnkd.in/djy5iDKE 8. DP problems summary (problem categorization) - https://lnkd.in/dbUrGV3C 9. Categorization of Leetcode DP problems - https://lnkd.in/dMqRZYrZ 10. Must do Dynamic Programming Category wise - https://lnkd.in/dFpneDKY 11. Dynamic programming is simple - https://lnkd.in/dk4emjPt 12. Dynamic Programming on subsets with examples - https://lnkd.in/dQBhzxBV

๐—–๐—ถ๐˜€๐—ฐ๐—ผ ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿ˜ Upgrade Your Tech Skills in 2025โ€”For FREE! ๐Ÿ”น Introduction t
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Python Cheatsheet-4.pdf1.53 MB

๐—•๐—ฟ๐—ฒ๐—ฎ๐—ธ ๐—œ๐—ป๐˜๐—ผ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—™๐—ฅ๐—˜๐—˜ โ€“ ๐—ก๐—ผ ๐—˜๐˜…๐—ฐ๐˜‚๐˜€๐—ฒ๐˜€!๐Ÿ˜ Want to learn Data Analytics, Python
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Important questions to ace your machine learning interview with an approach to answer: 1. Machine Learning Project Lifecycle:    - Define the problem    - Gather and preprocess data    - Choose a model and train it    - Evaluate model performance    - Tune and optimize the model    - Deploy and maintain the model 2. Supervised vs Unsupervised Learning:    - Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).    - Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments). 3. Evaluation Metrics for Regression:    - Mean Absolute Error (MAE)    - Mean Squared Error (MSE)    - Root Mean Squared Error (RMSE)    - R-squared (coefficient of determination) 4. Overfitting and Prevention:    - Overfitting: Model learns the noise instead of the underlying pattern.    - Prevention: Use simpler models, cross-validation, regularization. 5. Bias-Variance Tradeoff:    - Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity. 6. Cross-Validation:    - Technique to assess model performance by splitting data into multiple subsets for training and validation. 7. Feature Selection Techniques:    - Filter methods (e.g., correlation analysis)    - Wrapper methods (e.g., recursive feature elimination)    - Embedded methods (e.g., Lasso regularization) 8. Assumptions of Linear Regression:    - Linearity    - Independence of errors    - Homoscedasticity (constant variance)    - No multicollinearity 9. Regularization in Linear Models:    - Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients. 10. Classification vs Regression:     - Classification: Predicts a categorical outcome (e.g., class labels).     - Regression: Predicts a continuous numerical outcome (e.g., house price). 11. Dimensionality Reduction Algorithms:     - Principal Component Analysis (PCA)     - t-Distributed Stochastic Neighbor Embedding (t-SNE) 12. Decision Tree:     - Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes. 13. Ensemble Methods:     - Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting). 14. Handling Missing or Corrupted Data:     - Imputation (e.g., mean substitution)     - Removing rows or columns with missing data     - Using algorithms robust to missing values 15. Kernels in Support Vector Machines (SVM):     - Linear kernel     - Polynomial kernel     - Radial Basis Function (RBF) kernel Data Science Interview Resources ๐Ÿ‘‡๐Ÿ‘‡ https://topmate.io/coding/914624 Like for more ๐Ÿ˜„

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How Promotion in TCS takes place ๐Ÿ‘‡๐Ÿ‘‡ https://datasimplifier.com/promotion-in-tcs/

๐Ÿ”ฐ Complete Python Handwritten Notes! Sharing this file again cause some people are getting problems to download this book! React โ€œโค๏ธโ€ if you want more ebooks & notes

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