Top 20 AI Concepts You Should Know

1 - Machine Learning: Core algorithms, statistics, and model training techniques.
2 - Deep Learning: Hierarchical neural networks learning complex representations automatically.
3 - Neural Networks: Layered architectures efficiently model nonlinear relationships accurately.
4 - NLP: Techniques to process and understand natural language text.
5 - Computer Vision: Algorithms interpreting and analyzing visual data effectively
6 - Reinforcement Learning: Distributed traffic across multiple servers for reliability.
7 - Generative Models: Creating new data samples using learned data.
8 - LLM: Generates human-like text using massive pre-trained data.
9 - Transformers: Self-attention-based architecture powering modern AI models.
10 - Feature Engineering: Designing informative features to improve model performance significantly.
11 - Supervised Learning: Learns useful representations without labeled data.
12 - Bayesian Learning: Incorporate uncertainty using probabilistic model approaches.
13 - Prompt Engineering: Crafting effective inputs to guide generative model outputs.
14 - AI Agents: Autonomous systems that perceive, decide, and act.
15 - Fine-Tuning Models: Customizes pre-trained models for domain-specific tasks.
16 - Multimodal Models: Processes and generates across multiple data types like images, videos, and text.
17 - Embeddings: Transforms input into machine-readable vector formats.
18 - Vector Search: Finds similar items using dense vector embeddings.
19 - Model Evaluation: Assessing predictive performance using validation techniques.
20 - AI Infrastructure: Deploying scalable systems to support AI operations.

Artificial intelligence Resources: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E

AI Jobs: https://whatsapp.com/channel/0029VaxtmHsLikgJ2VtGbu1R

Hope this helps you ☺️

List of Top 12 Coding Channels on WhatsApp:

1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L


2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17


3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502


4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X


5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s


6. Javanoscript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32


7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z


8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E


9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y


10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D


11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v


12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43

ENJOY LEARNING 👍👍

Our world will soon change forever! Are you ready? Read the Manifesto  https://aism.faith to understand the future ahead, subscribe to the channel: https://news.1rj.ru/str/aism

𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝘃𝘀 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝘃𝘀 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 — 𝗪𝗵𝗶𝗰𝗵 𝗣𝗮𝘁𝗵 𝗶𝘀 𝗥𝗶𝗴𝗵𝘁 𝗳𝗼𝗿 𝗬𝗼𝘂? 🤔

In today’s data-driven world, career clarity can make all the difference. Whether you’re starting out in analytics, pivoting into data science, or aligning business with data as an analyst — understanding the core responsibilities, skills, and tools of each role is crucial.

🔍 Here’s a quick breakdown from a visual I often refer to when mentoring professionals:

🔹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁

󠁯•󠁏 Focus: Analyzing historical data to inform decisions.

󠁯•󠁏 Skills: SQL, basic stats, data visualization, reporting.

󠁯•󠁏 Tools: Excel, Tableau, Power BI, SQL.

🔹 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁

󠁯•󠁏 Focus: Predictive modeling, ML, complex data analysis.

󠁯•󠁏 Skills: Programming, ML, deep learning, stats.

󠁯•󠁏 Tools: Python, R, TensorFlow, Scikit-Learn, Spark.

🔹 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁

󠁯•󠁏 Focus: Bridging business needs with data insights.

󠁯•󠁏 Skills: Communication, stakeholder management, process modeling.

󠁯•󠁏 Tools: Microsoft Office, BI tools, business process frameworks.

👉 𝗠𝘆 𝗔𝗱𝘃𝗶𝗰𝗲:

Start with what interests you the most and aligns with your current strengths. Are you business-savvy? Start as a Business Analyst. Love solving puzzles with data?

Explore Data Analyst. Want to build models and uncover deep insights? Head into Data Science.

🔗 𝗧𝗮𝗸𝗲 𝘁𝗶𝗺𝗲 𝘁𝗼 𝘀𝗲𝗹𝗳-𝗮𝘀𝘀𝗲𝘀𝘀 𝗮𝗻𝗱 𝗰𝗵𝗼𝗼𝘀𝗲 𝗮 𝗽𝗮𝘁𝗵 𝘁𝗵𝗮𝘁 𝗲𝗻𝗲𝗿𝗴𝗶𝘇𝗲𝘀 𝘆𝗼𝘂, not just one that’s trending.

Please go through this top 10 SQL projects with Datasets that you can practice and can add in your resume

📌1. Social Media Analytics:
(https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset)

🚀2. Web Analytics:
(https://www.kaggle.com/zynicide/wine-reviews)

📌3. HR Analytics:
(https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-
attrition-dataset)

🚀4. Healthcare Data Analysis:
(https://www.kaggle.com/cdc/mortality)

📌5. E-commerce Analysis:
(https://www.kaggle.com/olistbr/brazilian-ecommerce)

🚀6. Inventory Management:
(https://www.kaggle.com/datasets?
search=inventory+management)

📌 7.Customer Relationship Management:
(https://www.kaggle.com/pankajjsh06/ibm-watson-
marketing-customer-value-data)

🚀8. Financial Data Analysis:
(https://www.kaggle.com/awaiskalia/banking-database)

📌9. Supply Chain Management:
(https://www.kaggle.com/shashwatwork/procurement-analytics)

🚀10. Analysis of Sales Data:
(https://www.kaggle.com/kyanyoga/sample-sales-data)

Small suggestion from my side for non tech students: kindly pick those datasets which you like the subject in general, that way you will be more excited to practice it, instead of just doing it for the sake of resume, you will learn SQL more passionately, since it’s a programming language try to make it more exciting for yourself.

Join for more: https://news.1rj.ru/str/DataPortfolio

Hope this piece of information helps you

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝘀𝗵𝗮𝗽𝗲 𝘆𝗼𝘂𝗿 𝗰𝗮𝗿𝗲𝗲𝗿: 👇

-> 1. Learn the Language of Data
Start with Python or R. Learn how to write clean noscripts, automate tasks, and manipulate data like a pro.

-> 2. Master Data Handling
Use Pandas, NumPy, and SQL. These are your weapons for data cleaning, transformation, and querying.
Garbage in = Garbage out. Always clean your data.

-> 3. Nail the Basics of Statistics & Probability
You can’t call yourself a data scientist if you don’t understand distributions, p-values, confidence intervals, and hypothesis testing.

-> 4. Exploratory Data Analysis (EDA)
Visualize the story behind the numbers with Matplotlib, Seaborn, and Plotly.
EDA is how you uncover hidden gold.

-> 5. Learn Machine Learning the Right Way

Start simple:

Linear Regression

Logistic Regression

Decision Trees
Then level up with Random Forest, XGBoost, and Neural Networks.


-> 6. Build Real Projects
Kaggle, personal projects, domain-specific problems—don’t just learn, apply.
Make a portfolio that speaks louder than your resume.

-> 7. Learn Deployment (Optional but Powerful)
Use Flask, Streamlit, or FastAPI to deploy your models.
Turn models into real-world applications.

-> 8. Sharpen Soft Skills
Storytelling, communication, and business acumen are just as important as technical skills.
Explain your insights like a leader.


𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗵𝗮𝘃𝗲 𝘁𝗼 𝗯𝗲 𝗽𝗲𝗿𝗳𝗲𝗰𝘁.
𝗬𝗼𝘂 𝗷𝘂𝘀𝘁 𝗵𝗮𝘃𝗲 𝘁𝗼 𝗯𝗲 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁.

Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

Like if you need similar content 😄👍

Hope this helps you 😊

FREE RESOURCES TO LEARN MACHINE LEARNING
👇👇

Intro to ML by MIT Free Course

https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/about

Machine Learning for Everyone FREE BOOK

https://buildmedia.readthedocs.org/media/pdf/pymbook/latest/pymbook.pdf

ML Crash Course by Google

https://developers.google.com/machine-learning/crash-course

Advanced Machine Learning with Python Github

https://github.com/PacktPublishing/Advanced-Machine-Learning-with-Python

Practical Machine Learning Tools and Techniques Free Book

https://vk.com/doc10903696_437487078?hash=674d2f82c486ac525b&dl=ed6dd98cd9d60a642b

Python Machine Learning for beginners

https://news.1rj.ru/str/datasciencefun/1177

ENJOY LEARNING 👍👍

Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months

### Week 1: Introduction to Python

Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions

Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)

Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules

Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode

### Week 2: Advanced Python Concepts

Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions

Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files

Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation

Day 14: Practice Day
- Solve intermediate problems on coding platforms

### Week 3: Introduction to Data Structures

Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists

Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues

Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions

Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues

### Week 4: Fundamental Algorithms

Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort

Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis

Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques

Day 28: Practice Day
- Solve problems on sorting, searching, and hashing

### Week 5: Advanced Data Structures

Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)

Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps

Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)

Day 35: Practice Day
- Solve problems on trees, heaps, and graphs

### Week 6: Advanced Algorithms

Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)

Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms

Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree

Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms

### Week 7: Problem Solving and Optimization

Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems

Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef

Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization

Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them

### Week 8: Final Stretch and Project

Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts

Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project

Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems

Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report

Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)

Best DSA RESOURCES: https://topmate.io/coding/886874

Credits: https://news.1rj.ru/str/free4unow_backup

ENJOY LEARNING 👍👍

🔗 SQL JOINS (INNER, LEFT, RIGHT, FULL, SELF)

JOINS help you combine data from two or more tables based on a related column (usually a primary key and a foreign key).

1. INNER JOIN
Returns only matching rows between two tables.

SELECT customers.name, orders.order_id
FROM customers
INNER JOIN orders ON customers.id = orders.customer_id;

This returns only those customers who have placed at least one order.

2. LEFT JOIN (or LEFT OUTER JOIN)
Returns all rows from the left table, and matched rows from the right table. If no match, you'll see NULLs.

SELECT customers.name, orders.order_id
FROM customers
LEFT JOIN orders ON customers.id = orders.customer_id;

This shows all customers, including those who haven’t placed any orders.

3. RIGHT JOIN (or RIGHT OUTER JOIN)
Returns all rows from the right table, and matching rows from the left.

SELECT customers.name, orders.order_id
FROM customers
RIGHT JOIN orders ON customers.id = orders.customer_id;

You’ll see all orders — even if there’s no corresponding customer info.

4. FULL JOIN (or FULL OUTER JOIN)
Returns all rows from both tables. If there's no match, it returns NULLs.

Note: MySQL doesn't support FULL JOIN directly; use UNION of LEFT and RIGHT joins instead.

5. SELF JOIN
You join a table with itself. Great for hierarchical relationships.

SELECT e.name AS employee, m.name AS manager
FROM employees e
JOIN employees m ON e.manager_id = m.id;

This shows each employee along with their manager's name.

Pro Tip: Be careful with NULLs and always define clear join conditions to avoid cartesian products.

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

📊 Data Science Essentials: What Every Data Enthusiast Should Know!

1️⃣ Understand Your Data
Always start with data exploration. Check for missing values, outliers, and overall distribution to avoid misleading insights.

2️⃣ Data Cleaning Matters
Noisy data leads to inaccurate predictions. Standardize formats, remove duplicates, and handle missing data effectively.

3️⃣ Use Denoscriptive & Inferential Statistics
Mean, median, mode, variance, standard deviation, correlation, hypothesis testing—these form the backbone of data interpretation.

4️⃣ Master Data Visualization
Bar charts, histograms, scatter plots, and heatmaps make insights more accessible and actionable.

5️⃣ Learn SQL for Efficient Data Extraction
Write optimized queries (SELECT, JOIN, GROUP BY, WHERE) to retrieve relevant data from databases.

6️⃣ Build Strong Programming Skills
Python (Pandas, NumPy, Scikit-learn) and R are essential for data manipulation and analysis.

7️⃣ Understand Machine Learning Basics
Know key algorithms—linear regression, decision trees, random forests, and clustering—to develop predictive models.

8️⃣ Learn Dashboarding & Storytelling
Power BI and Tableau help convert raw data into actionable insights for stakeholders.

🔥 Pro Tip: Always cross-check your results with different techniques to ensure accuracy!

Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

DOUBLE TAP ❤️ IF YOU FOUND THIS HELPFUL!

🚀 Required Skills for a data scientist

🎯Statistics and Probability
🎯Mathematics
🎯Python, R, SAS and Scala or other.
🎯Data visualisation
🎯Big data
🎯Data inquisitiveness
🎯Business expertise
🎯Critical thinking
🎯Machine learning, deep learning and AI
🎯Communication skills
🎯Teamwork