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Machine Learning & Artificial Intelligence | Data Science Free Courses
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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun
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Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 66 657 obunachidan iborat bo'lib, Taสผlim toifasida 2 465-o'rinni va Malayziya mintaqasida 432-o'rinni egallagan.
๐ Auditoriya koโrsatkichlari va dinamika
ะฝะตะฒัะดะพะผะพ sanasidan buyon loyiha tez oโsib, 66 657 obunachiga ega boโldi.
21 Iyun, 2026 dagi oxirgi maโlumotlarga koโra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 571 ga, soโnggi 24 soatda esa 2 ga oโzgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya oโrtacha 0.92% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.79% ini tashkil etuvchi reaksiyalarni toโplaydi.
- Post qamrovi: Har bir post oโrtacha 612 marta koโriladi; birinchi sutkada odatda 524 ta koโrish yigโiladi.
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- Tematik yoโnalishlar: Kontent sellerflash, waybienad, pricing, buybox, buyer kabi asosiy mavzularga jamlangan.
๐ Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโriflaydi:
โPerfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfunโ
Yuqori yangilanish chastotasi (oxirgi maโlumot 22 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโlib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taสผlim toifasidagi muhim taโsir nuqtasiga aylantirishini koโrsatadi.
66 657
Obunachilar
+224 soatlar
+417 kunlar
+57130 kunlar
Postlar arxiv
+9
Data Science Topics ๐
๐ช๐ฎ๐ป๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐? ๐๐ฒ๐ฟ๐ฒโ๐ ๐๐ผ๐!๐
Learn AI from scratch with these 6 YouTube channels! ๐ฏ
๐กWhether youโre a beginner or an AI enthusiast, these top AI experts will guide you through AI fundamentals, deep learning, and real-world applications
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https://pdlink.in/4iIxCy8
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๐ Exercises in Machine Learning
Book
Skills for Data Scientists ๐
๐ช๐ฎ๐ป๐ ๐๐ผ ๐บ๐ฎ๐๐๐ฒ๐ฟ ๐๐
๐ฐ๐ฒ๐น ๐ถ๐ป ๐ท๐๐๐ ๐ณ ๐ฑ๐ฎ๐๐?
๐ Here's a structured roadmap to help you go from beginner to pro in a week!
Whether you're learning formulas, functions, or data visualization, this guide covers everything step by step.
๐๐ข๐ง๐ค๐ :-
https://pdlink.in/43lzybE
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Tech stack for Machine Learning in 2024:
- ml workflow orchestrator: Kubeflow
- experiment tracking: MLflow
- data ingestion: Airbyte
- job orchestrator: Apache Airflow
- batch pipeline: Apache Spark
- message queue for real-time streaming: Apache Kafka
- feature engineering: Scikit-learn
- model selection and training: Pytorch
- hyperparameter tuning: Ray Tune
- model evaluation: Weights & Biases
- model monitoring: Grafana
- CI/CD: Github actions
- model versioning: neptune
- model serving: BentoML
- web app framework: Flask
- front-end: React
- feature store: Qwak
- Graph database: Neo4j
- Vector database: ChromaDB
- NoSQL database: MongoDB
- In-memory data store: Redis
...
What is your current ML tech stack?
This post is for beginners who decided to learn Data Science. I want to tell you that becoming a data scientist is a journey (6 months - 1 year at least) and not a 1 month thing where u do some courses and you are a data scientist. There are different fields in Data Science that you have to first get familiar and strong in basics as well as do hands-on to get the abilities that are required to function in a full time job opportunity. Then further delve into advanced implementations.
There are plenty of roadmaps and online content both paid and free that you can follow. In a nutshell. A few essential things that will be necessary and in no particular order that will at least get your data science journey started are below:
Basic Statistics, Linear Algebra, calculus, probability
Programming language (R or Python) - Preferably Python if you rather want to later on move into a developer role instead of sticking to data science.
Machine Learning - All of the above will be used here to implement machine learning concepts.
Data Visualisation - again it could be simple excel or via r/python libraries or tools like Tableau,PowerBI etc.
This can be overwhelming but again its just an indication of what lies ahead. So most important thing is to just START instead of just contemplating the best way to go about this. Since lot of things can be learnt independently as well in no particular order.
You can use the below Sources to prepare your own roadmap:
@free4unow_backup - some free courses from here
@datasciencefun - check & search in this channel with #freecourses
Data Science - https://365datascience.pxf.io/q4m66g
Python - https://bit.ly/45rlWZE
Kaggle - https://www.kaggle.com/learn
๐๐ฅ๐๐ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐! ๐๐
Want to master data analytics? Here are top free courses, books, and certifications to help you get started with Power BI, Tableau, Python, and Excel.
๐๐ข๐ง๐ค๐
https://pdlink.in/41Fx3PW
All The Best ๐ฅ
