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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 68 675 obunachidan iborat bo'lib, Taʼlim toifasida 2 398-o'rinni va Malayziya mintaqasida 418-o'rinni egallagan.

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26 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 257 ga, so‘nggi 24 soatda esa 8 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

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Skills for Data Scientists 👆

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𝗪𝗮𝗻𝘁 𝘁𝗼 𝗺𝗮𝘀𝘁𝗲𝗿 𝗘𝘅𝗰𝗲𝗹 𝗶𝗻 𝗷𝘂𝘀𝘁 𝟳 𝗱𝗮𝘆𝘀? 📊 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. 𝐋𝐢𝐧𝐤👇 :- https://pdlink.in/43lzybE All The Best 💥

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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?

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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

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𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀! 📊🚀 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. 𝐋𝐢𝐧𝐤👇 https://pdlink.in/41Fx3PW All The Best 💥

68 673