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🔰 Important Built-in Functions in Python

Essential Skills to Master for Using Generative AI

1️⃣ Prompt Engineering
✍️ Learn how to craft clear, detailed prompts to get accurate AI-generated results.

2️⃣ Data Literacy
📊 Understand data sources, biases, and how AI models process information.

3️⃣ AI Ethics & Responsible Usage
⚖️ Know the ethical implications of AI, including bias, misinformation, and copyright issues.

4️⃣ Creativity & Critical Thinking
💡 AI enhances creativity, but human intuition is key for quality content.

5️⃣ AI Tool Familiarity
🔍 Get hands-on experience with tools like ChatGPT, DALL·E, Midjourney, and Runway ML.

6️⃣ Coding Basics (Optional)
💻 Knowing Python, SQL, or APIs helps customize AI workflows and automation.

7️⃣ Business & Marketing Awareness
📢 Leverage AI for automation, branding, and customer engagement.

8️⃣ Cybersecurity & Privacy Knowledge
🔐 Learn how AI-generated data can be misused and ways to protect sensitive information.

9️⃣ Adaptability & Continuous Learning
🚀 AI evolves fast—stay updated with new trends, tools, and regulations.

Master these skills to make the most of AI in your personal and professional life! 🔥

Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U

Learn by Building: 60 GenAI Projects + AI Engineering Hub

1️⃣ 60 ready projects on generative AI

List of 60 projects on GitHub with open code on generative AI from text models to audio and video.

Each project includes a description and a link to the repository. You can choose an idea, run it locally, and build your own AI portfolio.

Github and Even more useful stuff.

2️⃣ AI Engineering Hub : comprehensive resource for learning & developing AI-based solutions.

- 93+ production-ready projects for any level
- detailed tutorials on LLM, RAG, agents, and much more
- real examples of AI agent applications
- ready-made examples for implementation, adaptation, and scaling in your projects

Grab it on GitHub

📊 Data Science Roadmap at a Glance

Master the key pillars of Data Science step by step:

• Math & Stats: Build foundations in Linear Algebra, Probability, and Hypothesis Testing.

• Programming: Learn Python/R and SQL for data handling and analysis.

• Visualization: Use Tableau, Power BI, or Excel to tell stories with data.

• Feature Engineering: Focus on feature selection, encoding, and generation.

• Machine Learning: Start with basics, then explore advanced models like XGBoost.

• Deep Learning: Dive into Neural Networks, CNNs, and RNNs with TensorFlow or PyTorch.

• NLP: Work with text data using classification and word embeddings.

• Deployment: Deploy models using Flask, Django, or cloud platforms.

🎯 Tip: Learn consistently — Data Science is a journey, not a sprint.

The program for the 10th AI Journey 2025 international conference has been unveiled: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it!

Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus from around the world!

On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future.

On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.

On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today!

Ride the wave with AI into the future!

Tune in to the AI Journey webcast on November 19-21.

DATA ANALYST Interview Questions (0-3 yr) (SQL, Power BI)

👉 Power BI:

Q1: Explain step-by-step how you will create a sales dashboard from scratch.

Q2: Explain how you can optimize a slow Power BI report.

Q3: Explain Any 5 Chart Types and Their Uses in Representing Different Aspects of Data.

👉SQL:

Q1: Explain the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() functions using example.

Q2 – Q4 use Table: employee (EmpID, ManagerID, JoinDate, Dept, Salary)

Q2: Find the nth highest salary from the Employee table.

Q3: You have an employee table with employee ID and manager ID. Find all employees under a specific manager, including their subordinates at any level.

Q4: Write a query to find the cumulative salary of employees department-wise, who have joined the company in the last 30 days.

Q5: Find the top 2 customers with the highest order amount for each product category, handling ties appropriately. Table: Customer (CustomerID, ProductCategory, OrderAmount)

👉Behavioral:

Q1: Why do you want to become a data analyst and why did you apply to this company?

Q2: Describe a time when you had to manage a difficult task with tight deadlines. How did you handle it?

I have curated best top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Hope this helps you 😊

Top 6 Data Concepts

Tune in to the 10th AI Journey 2025 international conference: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it!

Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus! Do you agree with their predictions about AI?

On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.

On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! The day's program includes presentations by scientists from around the world:
- Ajit Abraham (Sai University, India) will present on “Generative AI in Healthcare”
- Nebojša Bačanin Džakula (Singidunum University, Serbia) will talk about the latest advances in bio-inspired metaheuristics
- AIexandre Ferreira Ramos (University of São Paulo, Brazil) will present his work on using thermodynamic models to study the regulatory logic of transcriptional control at the DNA level
- Anderson Rocha (University of Campinas, Brazil) will give a presentation entitled “AI in the New Era: From Basics to Trends, Opportunities, and Global Cooperation”.

And in the special AIJ Junior track, we will talk about how AI helps us learn, create and ride the wave with AI.

The day will conclude with an award ceremony for the winners of the AI Challenge for aspiring data scientists and the AIJ Contest for experienced AI specialists. The results of an open selection of AIJ Science research papers will be announced.

Ride the wave with AI into the future!

Tune in to the AI Journey webcast on November 19-21.

Data Science Techniques

Data Analytics Interview Questions

1. What is the difference between SQL and MySQL?

SQL is a standard language for retrieving and manipulating structured databases. On the contrary, MySQL is a relational database management system, like SQL Server, Oracle or IBM DB2, that is used to manage SQL databases.


2. What is a Cross-Join?

Cross join can be defined as a cartesian product of the two tables included in the join. The table after join contains the same number of rows as in the cross-product of the number of rows in the two tables. If a WHERE clause is used in cross join then the query will work like an INNER JOIN.


3. What is a Stored Procedure?

A stored procedure is a subroutine available to applications that access a relational database management system (RDBMS). Such procedures are stored in the database data dictionary. The sole disadvantage of stored procedure is that it can be executed nowhere except in the database and occupies more memory in the database server.


4. What is Pattern Matching in SQL?

SQL pattern matching provides for pattern search in data if you have no clue as to what that word should be. This kind of SQL query uses wildcards to match a string pattern, rather than writing the exact word. The LIKE operator is used in conjunction with SQL Wildcards to fetch the required information.

Sometimes reality outpaces expectations in the most unexpected ways.
While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included.
✅ No API paywalls.
✅ No usage restrictions.
✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs.

What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers.

GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments.
GitHub | HuggingFace | GitVerse

GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count.
Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support.
GitHub | Hugging Face | GitVerse

Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation.
GitHub | GitVerse | Hugging Face | Technical report

Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech.
GitHub | HuggingFace | GitVerse

Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.

✅ Useful Resources to Learn Machine Learning in 2025 🤖📘

1. YouTube Channels
⦁ StatQuest – Simple, visual ML explanations
⦁ Krish Naik – ML projects and interviews
⦁ Simplilearn – Concepts + hands-on demos
⦁ freeCodeCamp – Full ML crash courses

2. Free Courses
⦁ Andrew Ng’s ML – Coursera (audit for free)
⦁ Google’s ML Crash Course – Interactive + videos
⦁ Kaggle Learn – Short, hands-on ML tutorials
⦁ Fast.ai – Practical deep learning for coders

3. Practice Platforms
⦁ Kaggle – Real datasets, notebooks, and competitions
⦁ Google Colab – Run Python ML code in browser
⦁ DrivenData – ML competitions with impact

4. Projects to Try
⦁ House price predictor
⦁ Stock trend classifier
⦁ Sentiment analysis on tweets
⦁ MNIST handwritten digit recognition
⦁ Recommendation system

5. Key Libraries
⦁ scikit-learn – Core ML algorithms
⦁ pandas – Data manipulation
⦁ matplotlib/seaborn – Visualization
⦁ TensorFlow / PyTorch – Deep learning
⦁ XGBoost – Advanced boosting models

6. Must-Know Concepts
⦁ Supervised vs Unsupervised learning
⦁ Overfitting & underfitting
⦁ Model evaluation: Accuracy, F1, ROC
⦁ Cross-validation
⦁ Feature engineering

7. Books
⦁ “Hands-On ML with Scikit-Learn & TensorFlow” – Aurélien Géron
⦁ “Python ML” – Sebastian Raschka

💡 Build a portfolio. Learn by doing. Share projects on GitHub.

💬 Tap ❤️ for more!