🎉💯2024 Highly demanded Top 100+ IT Training courses FREE Giveaway in Networking, Project Management, Cloud and Cyber security including #CCNA 200-301, #CCNP 350-401 #Comptia, #PMP, #AWS, #Azure #Python, #Excel, #AI, #Google courses...... ⬇️📕

✨Get now & start whenever you want! Don't miss this chance to kickstart your IT career in 2024!✨

🔗👨‍💻Free CCNA Training Course: https://bit.ly/3BoYEdH
🔗🗒️Enroll Free Online Course: https://bit.ly/4dru404
🔗📝Download Free #IT Study Materials:https://bit.ly/3Y213Uj

🔗📲Contact for 1v1 IT Certs Exam Help: https://wa.link/k0vy3x
🌐📚 JOIN IT Study GROUP to Get Madness Discount 👇: https://chat.whatsapp.com/HqzBlMaOPci0wYvkEtcCDa

🔎Follow Social Media for Free e-Book:
https://linktr.ee/SPOTOSocialMedia

🚀 Boost Your IT Exam Prep with SPOTO's FREE Study Materials! 🎉

💡 Ready to Pass Your IT Exam?
SPOTO is here to help you succeed! Get SPOTO FREE IT study materials to jumpstart your certification journey. Whether you're preparing for #Cisco, #AWS, #PMP, #Python, #Excel, #Google, #Microsoft, or other certifications, we've got you covered.

🔗🎒Download Free IT Certs Exam E-book: https://bit.ly/4fJSoLP

🔗👩‍💻Test Your IT Skills for Free: https://bit.ly/3PoKH39

🔗📝Download Free Cloud Certs Study Materials:https://bit.ly/4gI4KWk

🔗📲Contact for 1v1 IT Certs Exam Help: https://wa.link/k0vy3x
🌐📚 JOIN IT Study GROUP👇: https://chat.whatsapp.com/E3Vkxa19HPO9ZVkWslBO8s

Some people asked me about a resource for learning about Transformers.

Here's a good one I am sharing again -- it covers just about everything you need to know.

brandonrohrer.com/transformers

Amazing stuff. It's totally worth your weekend.

#Transformers #DeepLearning #NLP #AI #MachineLearning #SelfAttention #DataScience #Technology #Python #LearningResource

https://t.iss.one/CodeProgrammer

The Hundred-Page Language Models Book

Read it:
https://github.com/aburkov/theLMbook

#LLM #NLP #ML #AI #PYTHON #PYTORCH

https://t.iss.one/DataScienceM

🎓 2025 Top IT Certification – Free Study Materials Are Here!

🔥Whether you're preparing for #Cisco #AWS #PMP #Python #Excel #Google #Microsoft #AI or any other in-demand certification – SPOTO has got you covered!

📘 Download the FREE IT Certs Exam E-book:
👉 https://bit.ly/4lNVItV
🧠 Test Your IT Skills for FREE:
👉 https://bit.ly/4imEjW5
☁️ Download Free AI Materials :
👉 https://bit.ly/3F3lc5B

📞 Need 1-on-1 IT Exam Help? Contact Now:
👉 https://wa.link/k0vy3x
🌐 Join Our IT Study Group for Daily Updates & Tips:
👉 https://chat.whatsapp.com/E3Vkxa19HPO9ZVkWslBO8s

🎯 ابدأ رحلتك الاحترافية في البرمجة مع
#Python_Mastery_Course 🐍
هل ترغب بتعلم لغة البرمجة الأكثر طلبًا في العالم؟
هل تحلم بالوصول إلى مجالات مثل الذكاء الاصطناعي، تحليل البيانات أو تصميم الواجهات؟
📢 هذه الدورة خُصصت لتكون نقطة انطلاقك نحو المستقبل!
________________________________________
🚀 ماذا ستتعلم في هذه الدورة؟
🔹 الوحدة 1: أساسيات بايثون (المتغيرات – أنواع البيانات – العمليات – أساسيات الكود)
🔹 الوحدة 2: التحكم في سير البرنامج (الشروط – الحلقات – أوامر التحكم)
🔹 الوحدة 3: هياكل البيانات (قوائم – قواميس – مجموعات – Tuples)
🔹 الوحدة 4: الدوال (إنشاء – معاملات – النطاق – التكرار)
🔹 الوحدة 5: الوحدات (Modules)
🔹 الوحدة 6: التعامل مع الملفات وملفات CSV
🔹 الوحدة 7: معالجة الاستثناءات باحتراف
🔹 الوحدة 8: البرمجة الكائنية (OOP)
🔹 الوحدة 9: المفاهيم المتقدمة:
  ✅ المولدات (Generators)
  ✅ الكائنات القابلة للتكرار (Iterators)
  ✅ المزينات (Decorators)
💡 عند انتهائك ستكون قادرًا على:
✔️ بناء مشاريع حقيقية بلغة بايثون
✔️ الانتقال بثقة إلى مجالات متقدمة مثل الذكاء الاصطناعي وتحليل البيانات
✔️ أتمتة المهام والتعامل مع البيانات باحتراف

🎥 نظام الدورة:
• بث مباشر Live مع المدرب د. محمد عماد عرفه
• جميع المحاضرات ستُرفع على الموقع لتشاهدها في الوقت الذي يناسبك
🕒 مدة الدورة: 25 ساعة تدريبية
📅 تاريخ البداية:15- 6
💰 خصم للحجز المبكر
تواصل الآن مع ذكر كود الدورة"001"
https://t.iss.one/Agartha_Support

🚀 FREE IT Study Kits for 2025 — Grab Yours Now!

Just found these zero-cost resources from SPOTO👇
Perfect if you're prepping for #Cisco, #AWS, #PMP, #AI, #Python, #Excel, or #Cybersecurity!
✅ 100% Free
✅ No signup traps
✅ Instantly downloadable

📘 IT Certs E-book: https://bit.ly/4fJSoLP
☁️ Cloud & AI Kits: https://bit.ly/3F3lc5B
📊 Cybersecurity, Python & Excel: https://bit.ly/4mFrA4g
🧠 Skill Test (Free!): https://bit.ly/3PoKH39
Tag a friend & level up together 💪

🌐 Join the IT Study Group: https://chat.whatsapp.com/E3Vkxa19HPO9ZVkWslBO8s
📲 1-on-1 Exam Help: https://wa.link/k0vy3x
👑Last 24 HOURS to grab Mid-Year Mega Sale prices！Don’t miss Lucky Draw👇
https://bit.ly/43VgcbT

🚀 2025 FREE Study Recourses from SPOTO for y’all — Don’t Miss Out!
✅ 100% Free Downloads
✅ No signup / spam

📘 #Python, Cybersecurity & Excel: https://bit.ly/4lYeVYp
📊 #Cloud Computing: https://bit.ly/45Rj1gm
☁️ #AI Kits: https://bit.ly/4m4bHTc
🔐 #CCNA Courses: https://bit.ly/45TL7rm
🧠 Free Online Practice – Test Now: https://bit.ly/41Kurjr

September 8th to 21th, SPOTO launches the Lowest Price Ever on ALL products! 🔥
Amazing Discounts for 📌 CCNA 200-301 📌 CCNP 400-007 and more…
📲 Contact admin to grab them: https://wa.link/uxde01

💡 ViT for Fashion MNIST Classification

This lesson demonstrates how to use a pre-trained Vision Transformer (ViT) to classify an image from the Fashion MNIST dataset. ViT treats an image as a sequence of patches, similar to how language models treat sentences, making it a powerful architecture for computer vision tasks. We will use a model from the Hugging Face Hub that is already fine-tuned for this specific dataset.

from transformers import ViTImageProcessor, ViTForImageClassification
from datasets import load_dataset
import torch

# 1. Load a model fine-tuned on Fashion MNIST and its processor
model_name = "abhishek/autotrain-fashion-mnist-283834433"
processor = ViTImageProcessor.from_pretrained(model_name)
model = ViTForImageClassification.from_pretrained(model_name)

# 2. Load the dataset and get a sample image
dataset = load_dataset("fashion_mnist", split="test")
image = dataset[100]['image'] # Get the 100th image

# 3. Preprocess the image and prepare it for the model
inputs = processor(images=image, return_tensors="pt")

# 4. Perform inference to get the classification logits
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits

# 5. Get the predicted class and its label
predicted_class_idx = logits.argmax(-1).item()
predicted_class = model.config.id2label[predicted_class_idx]

print(f"Image is a: {dataset[100]['label']}")
print(f"Model predicted: {predicted_class}")

Code explanation: This script uses the transformers library to load a ViT model specifically fine-tuned for Fashion MNIST classification. It then loads the dataset, selects a single sample image, and uses the model's processor to convert it into the correct input format. The model performs inference, and the script identifies the most likely class from the output logits, printing the final human-readable prediction.

#Python #MachineLearning #ViT #ComputerVision #HuggingFace

━━━━━━━━━━━━━━━
By: @DataScienceT ✨

💡 ViT for Fashion MNIST Classification

This lesson demonstrates how to use a pre-trained Vision Transformer (ViT) to classify an image from the Fashion MNIST dataset. ViT treats an image as a sequence of patches, similar to how language models treat sentences, making it a powerful architecture for computer vision tasks. We will use a model from the Hugging Face Hub that is already fine-tuned for this specific dataset.

from transformers import ViTImageProcessor, ViTForImageClassification
from datasets import load_dataset
import torch

# 1. Load a model fine-tuned on Fashion MNIST and its processor
model_name = "abhishek/autotrain-fashion-mnist-283834433"
processor = ViTImageProcessor.from_pretrained(model_name)
model = ViTForImageClassification.from_pretrained(model_name)

# 2. Load the dataset and get a sample image
dataset = load_dataset("fashion_mnist", split="test")
image = dataset[100]['image'] # Get the 100th image

# 3. Preprocess the image and prepare it for the model
inputs = processor(images=image, return_tensors="pt")

# 4. Perform inference to get the classification logits
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits

# 5. Get the predicted class and its label
predicted_class_idx = logits.argmax(-1).item()
predicted_class = model.config.id2label[predicted_class_idx]

print(f"Image is a: {dataset[100]['label']}")
print(f"Model predicted: {predicted_class}")

Code explanation: This script uses the transformers library to load a ViT model specifically fine-tuned for Fashion MNIST classification. It then loads the dataset, selects a single sample image, and uses the model's processor to convert it into the correct input format. The model performs inference, and the script identifies the most likely class from the output logits, printing the final human-readable prediction.

#Python #MachineLearning #ViT #ComputerVision #HuggingFace

━━━━━━━━━━━━━━━
By: @DataScienceT ✨

🤖🧠 Reflex: Build Full-Stack Web Apps in Pure Python — Fast, Flexible and Powerful

🗓️ 29 Oct 2025
📚 AI News & Trends

Building modern web applications has traditionally required mastering multiple languages and frameworks from JavaScript for the frontend to Python, Java or Node.js for the backend. For many developers, switching between different technologies can slow down productivity and increase complexity. Reflex eliminates that problem. It is an innovative open-source full-stack web framework that allows developers to ...

#Reflex #FullStack #WebDevelopment #Python #OpenSource #WebApps

Top 100 Data Analyst Interview Questions & Answers

#DataAnalysis #InterviewQuestions #SQL #Python #Statistics #CaseStudy #DataScience

Part 1: SQL Questions (Q1-30)

#1. What is the difference between DELETE, TRUNCATE, and DROP?
A:
• DELETE is a DML command that removes rows from a table based on a WHERE clause. It is slower as it logs each row deletion and can be rolled back.
• TRUNCATE is a DDL command that quickly removes all rows from a table. It is faster, cannot be rolled back, and resets table identity.
• DROP is a DDL command that removes the entire table, including its structure, data, and indexes.

#2. Select all unique departments from the employees table.
A: Use the DISTINCT keyword.

SELECT DISTINCT department
FROM employees;

#3. Find the top 5 highest-paid employees.
A: Use ORDER BY and LIMIT.

SELECT name, salary
FROM employees
ORDER BY salary DESC
LIMIT 5;

#4. What is the difference between WHERE and HAVING?
A:
• WHERE is used to filter records before any groupings are made (i.e., it operates on individual rows).
• HAVING is used to filter groups after aggregations (GROUP BY) have been performed.

-- Find departments with more than 10 employees
SELECT department, COUNT(employee_id)
FROM employees
GROUP BY department
HAVING COUNT(employee_id) > 10;

#5. What are the different types of SQL joins?
A:
• (INNER) JOIN: Returns records that have matching values in both tables.
• LEFT (OUTER) JOIN: Returns all records from the left table, and the matched records from the right table.
• RIGHT (OUTER) JOIN: Returns all records from the right table, and the matched records from the left table.
• FULL (OUTER) JOIN: Returns all records when there is a match in either the left or right table.
• SELF JOIN: A regular join, but the table is joined with itself.

#6. Write a query to find the second-highest salary.
A: Use OFFSET or a subquery.

-- Method 1: Using OFFSET
SELECT salary
FROM employees
ORDER BY salary DESC
LIMIT 1 OFFSET 1;

-- Method 2: Using a Subquery
SELECT MAX(salary)
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);

#7. Find duplicate emails in a customers table.
A: Group by the email column and use HAVING to find groups with a count greater than 1.

SELECT email, COUNT(email)
FROM customers
GROUP BY email
HAVING COUNT(email) > 1;

#8. What is a primary key vs. a foreign key?
A:
• A Primary Key is a constraint that uniquely identifies each record in a table. It must contain unique values and cannot contain NULL values.
• A Foreign Key is a key used to link two tables together. It is a field (or collection of fields) in one table that refers to the Primary Key in another table.

#9. Explain Window Functions. Give an example.
A: Window functions perform a calculation across a set of table rows that are somehow related to the current row. Unlike aggregate functions, they do not collapse rows.

-- Rank employees by salary within each department
SELECT
    name,
    department,
    salary,
    RANK() OVER (PARTITION BY department ORDER BY salary DESC) as dept_rank
FROM employees;

#10. What is a CTE (Common Table Expression)?
A: A CTE is a temporary, named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. It helps improve readability and break down complex queries.