Data Science & Machine Learning
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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
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📈 Telegram kanali Data Science & Machine Learning analitikasi
Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 77 661 obunachidan iborat bo'lib, Taʼlim toifasida 2 015-o'rinni va Hindiston mintaqasida 3 825-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 77 661 obunachiga ega bo‘ldi.
12 Noyabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -123 ga, so‘nggi 24 soatda esa -21 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 2.52% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.01% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 954 marta ko‘riladi; birinchi sutkada odatda 781 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent learning, accuracy, distribution, panda, dataset kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 13 Noyabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
77 661
Obunachilar
-2124 soatlar
-767 kun
-12330 kun
Postlar arxiv
Top 8 Github Repos to Learn Data Science and Python
1. All algorithms implemented in Python
By: The Algorithms
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Repo: https://github.com/TheAlgorithms/Python
2. DataScienceResources
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Repo: https://github.com/jonathan-bower/DataScienceResources
3. Playground and Cheatsheet for Learning Python
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4. Learn Python 3
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5. Awesome Data Science
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Repo: https://github.com/academic/awesome-datascience
6. data-scientist-roadmap
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Repo: https://github.com/MrMimic/data-scientist-roadmap
7. Data Science Best Resources
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Fork: 717
Repo: https://github.com/tirthajyoti/Data-science-best-resources/blob/master/README.md
8. Ds-cheatsheets
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Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals PDF
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Python for Data Science: The Ultimate Step-by-Step Guide to Learn Python In 7 Days & NLP, Data Science from with Python PDF
A LITTLE GUIDE TO HANDLING MISSING DATA
Having any Feature missing more than 5-10% of its values? you should consider it to be missing data or feature with high absence rate👀
How can you handle these missing values, ensuring you dont loose important part of your data🤷♀️
Not a problem😌. Here are important facts you must know😉
✍️Instances with missing values for all features should be eliminated
✍️Features with high absence rate should either be eliminated or filled with values
✍️Missing values can be replaced using Mean Imputation or Regression Imputation
✍️ Be careful with mean imputation for it may introduce bias as it evens out all instances
✍️Regression Imputation might overfit your model
✍️Mean and Regression Imputation can't be applied to Text features with missing values
✍️Text Features with missing values can be eliminated if not needed in data
✍️Important Text Features with Missing values can be replaced with a new class or category labelled as uncategorized
To become a Machine Learning Engineer:
• Python
• numpy, pandas, matplotlib, Scikit-Learn
• TensorFlow or PyTorch
• Jupyter, Colab
• Analysis > Code
• 99%: Foundational algorithms
• 1%: Other algorithms
• Solve problems ← This is key
• Teaching = 2 × Learning
• Have fun!
#numpy
NumPy
Smart use of ‘:’ to extract the right shape
Sometimes you encounter a 3-dim array that is of shape (N, T, D), while your function requires a shape of (N, D). At a time like this, reshape() will do more harm than good, so you are left with one simple solution:
Example:
for t in xrange(T):
x[:, t, :] = # ...📕 Introduction to Machine Learning
by Alex Smola and S.V.N. Vishwanathan
University Press, Cambridge
Expert_Python_Programming_Master_Python_by_learning_the_best_coding.epub4.52 MB
Pattern Recognition and
Machine Learning [ Information Science and Statistics ]
Christopher M. Bishop
#python #machinelearning #statistics #information #ai #ml
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