Data science/ML/AI

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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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Tarmoq:Programming, data science, ML - free courses by Big Data Specialist Hindiston29 106 Texnologiyalar & Aralashmalar8 932...

📈 Telegram kanali Data science/ML/AI analitikasi

Data science/ML/AI (@datascience_bds) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 13 899 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 8 932-o'rinni va Hindiston mintaqasida 29 106-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 13 899 obunachiga ega bo‘ldi.

27 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 81 ga, so‘nggi 24 soatda esa 1 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 8.01% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.06% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 1 113 marta ko‘riladi; birinchi sutkada odatda 287 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 panda, learning, row, api, ethic kabi asosiy mavzularga jamlangan.

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“Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 28 Avgust, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

13 899

Obunachilar

+124 soatlar

-97 kunlar

+8130 kunlar

1 113

Post ko'rishlar

~ 28724 soatlar

~ 41048 soatlar

8.01%

Muloqot nisbati

~ 1

Kuniga postlar

Ads index

beta

Postlar arxiv

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The Data Analyst Cheatsheet

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Cheatsheet: Imbalanced Data In Classification

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📚 Data Science Riddle You're building a chatbot but it gives generic answers. What's the root issue?

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Top ML Interview Questions & Answers.pdf1.42 KB

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Phases To Master Agentic AI

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Data Drift: The reason Good Models Go Bad You built a model that performed amazingly last month. Now? Accuracy tanked. Confusion Matrix looks like a crime scene. Welcome to Data Drift. The silent model killer. 📉 What Is Data Drift? It’s when the data your model sees today is different from the data it was trained on. Imagine you trained a model on pre-COVID shopping data then you tried to predict online purchases in 2021. People’s behavior changed. Your model didn’t. That’s drift. Reality shifted, but your math stayed still. 🧠 The Core Types ➡️ Covariate Drift: Input features change (e.g., user age distribution shifts). ➡️ Prior Drift: The target variable’s frequency changes (e.g., fewer defaults now). ➡️ Concept Drift: The relationship between input and output changes entirely. The last one is deadly. your model’s logic literally stops making sense. 🚨 Why It’s Dangerous Models decay quietly. By the time you notice lower performance, the damage( business or otherwise ) is already done. That’s why top teams monitor models like systems, not code. 🧩 The Fix 1. Track feature distributions over time (use KS test, PSI, or histograms). 2. Monitor prediction confidence — sudden uncertainty = red flag. 3. Retrain models periodically with fresh data. AI isn’t “build once.” It’s “maintain forever.”

A model is only as good as the world it was trained in and the world never stops changing.

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Comprehensive Feature Engineering Techniques

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📚 Data Science Riddle You're classifying product reviews (positive/negative). Which feature method is more effective for capturing context?

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Parameters vs Hyperparameters People confuse these all the time. Parameters: learned by the model during training. (e.g., weights in a neural network, coefficients in regression). Hyperparameters: set before training. They control how the model learns. (e.g., learning rate, number of layers, batch size). ✔️ Parameters = the student’s knowledge (changes as they study). ✔️ Hyperparameters = the teacher’s instructions (fixed rules of how to study). Tuning hyperparameters is often the difference between a good model and a useless one.

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DSA Cheatsheet

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📚 Data Science Riddle In Naive Bayes, what's the "naive" assumption?

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📚 Data Science Riddle You're training a hiring model. What's the biggest ethical risk?

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Cheatsheet: Ensemble Learning in ML

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cheatsheet-deep-learning.pdf3.35 KB

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AI/ML Cheatsheet

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Artificial Intelligence for Learning.pdf2.76 MB

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Softmax vs Sigmoid Functions Two of the most common activation functions… and two of the most misunderstood. Sigmoid: squashes input into a range between 0 and 1. Perfect for binary classification (yes/no problems). Example: spam or not spam. Softmax: takes a vector of numbers and turns them into probabilities that sum to 1. Perfect for multi-class classification (cat vs dog vs horse). 👉 Rule of thumb: Binary task → use Sigmoid. Multi-class task → use Softmax. Simple, but if you get this wrong, your model will never make sense.

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Data Visualization Cheatsheet

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Data Analyst 🆚 Data Engineer: Key Differences Confused about the roles of a Data Analyst and Data Engineer? 🤔 Here's a breakdown: 👨‍💻 Data Analyst: 🎯 Role: Analyzes, interprets, & visualizes data to extract insights for business decisions. 👍 Best For: Those who enjoy finding patterns, trends, & actionable insights. 🔑 Responsibilities: 🧹 Cleaning & organizing data. 📊 Using tools like Excel, Power BI, Tableau & SQL. 📝 Creating reports & dashboards. 🤝 Collaborating with business teams. Skills: Analytical skills, SQL, Excel, reporting tools, statistical analysis, business intelligence. ✅ Outcome: Guides decision-making in business, marketing, finance, etc. ⚙️ Data Engineer: 🏗️ Role: Designs, builds, & maintains data infrastructure. 👍 Best For: Those who enjoy technical data management & architecture for large-scale analysis. 🔑 Responsibilities: 🗄️ Managing databases & data pipelines. 🔄 Developing ETL processes. 🔒 Ensuring data quality & security. ☁️ Working with big data technologies like Hadoop, Spark, AWS, Azure & Google Cloud. Skills: Python, Java, Scala, database management, big data tools, data architecture, cloud technologies. ✅ Outcome: Creates infrastructure & pipelines for efficient data flow for analysis. In short: Data Analysts extract insights, while Data Engineers build the systems for data storage, processing, & analysis. Data Analysts focus on business outcomes, while Data Engineers focus on the technical foundation.

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📚 Data Science Riddle Why do CNNs use pooling layers?

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