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Data Science & Machine Learning

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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Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT ExpertTarmoq:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT ExpertHindiston4 204Taʼlim2 103

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Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 75 899 obunachidan iborat bo'lib, Taʼlim toifasida 2 103-o'rinni va Hindiston mintaqasida 4 204-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 75 899 obunachiga ega bo‘ldi.

23 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 731 ga, so‘nggi 24 soatda esa 33 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

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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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Postlar arxiv
Data Science & Machine Learning
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Hands-On Unsupervised Learning Using Python.pdf5.63 MB

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Python for Data Science Course @datasciencefun

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Machine learning using Python

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Top 50 Machine Learning Interview Q&A.pdf2.61 KB

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Lynda.com - Data Science Foundations - Fundamentals.zip665.81 MB

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Free ML webinar to learn how Swiggy uses Data Science! Link: https://bit.ly/3gNRBy0 ✅ Only for Indian users
Free ML webinar to learn how Swiggy uses Data Science! Link: https://bit.ly/3gNRBy0 ✅ Only for Indian users

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Would you prefer gradient boosting trees model or logistic regression when doing text classification with bag of words? Usually logistic regression is better because bag of words creates a matrix with large number of columns. For a huge number of columns logistic regression is usually faster than gradient boosting trees.

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MathforML.pdf5.00 MB

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Do you have a business idea? We are waiting for you! We are the first international channel PitchCamp, which prepared for you
Do you have a business idea? We are waiting for you! We are the first international channel PitchCamp, which prepared for you: ✔️Weekly opportunity to win $500 - $5000 just for business idea ✔️Daily educational tools & analytics ✔️Daily advises from our experts ✔️Live Performance from our active startups ✔️Up to $150.000 from our community for real start up Make the first step, get information and apply for weekly contest. 👉 PitchCamp

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Python Programming. Python Programming for Beginners, Python Programming for Intermediates

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Artificial Neural Networks with Java

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LinkedIn - Python for Data Science Essential Training Part 2.zip390.08 MB

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🌐 Join the researchers and programmers channel (Courses, Books, Papere and Codes). t.me/DataScience_Books

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Advanced ML with Python

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Introduction to Data Science - A Python Approach to Concepts, Techniques and Applications - Laura Igual, Santi Segui (Springer, 2017)

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What is unsupervised learning? Unsupervised learning aims to detect patterns in the data where no labels are given.

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Tableau_Cheatsheet.pdf1.65 KB

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git-cheat-sheet-education.pdf0.98 KB

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What are precision, recall, and F1-score? Precision and recall are classification evaluation metrics: P = TP / (TP + FP) and R = TP / (TP + FN). Where TP is true positives, FP is false positives and FN is false negatives In both cases the score of 1 is the best: we get no false positives or false negatives and only true positives. F1 is a combination of both precision and recall in one score (harmonic mean): F1 = 2 * PR / (P + R). Max F score is 1 and min is 0, with 1 being the best.

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How NASA Auto Colourise Images with Deep Learning? Free Live Sessions on Aug 19th,20th @7.00pm IST Register here : https://bi
How NASA Auto Colourise Images with Deep Learning? Free Live Sessions on Aug 19th,20th @7.00pm IST Register here : https://bit.ly/2SLpFlw