Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
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Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 68 146 obunachidan iborat bo'lib, Taʼlim toifasida 2 379-o'rinni va Hindiston mintaqasida 4 752-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 68 146 obunachiga ega bo‘ldi.
01 Sentabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 84 ga, so‘nggi 24 soatda esa 7 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 4.17% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.54% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 2 845 marta ko‘riladi; birinchi sutkada odatda 1 052 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 5 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent insidead, learning, degree, evaluation, algorithm kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 02 Sentabr, 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.
68 146
Obunachilar
+724 soatlar
+247 kun
+8430 kun
Postlar arxiv
Cheatsheet Machine Learning Algorithms
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Stanford's "Design and Analysis of Algorithms" Winter 2025
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"Data Structures & Algorithms using Python"
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Stanford’s Machine Learning - by Andrew Ng
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This book is for readers looking to learn new #machinelearning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different #algorithms create the models they do and the advantages and disadvantages of each one. This book will be most helpful for those with practice in basic modeling. It does not review best practices—such as feature engineering or balancing response variables—or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.https://dafriedman97.github.io/mlbook/content/introduction.html
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Introduction to Machine Learning Class Notes by Huy Nguyen
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