Artificial Intelligence on WhatsApp 🚀

Top AI Channels on WhatsApp!


1. ChatGPT – Your go-to AI for anything and everything. https://whatsapp.com/channel/0029VapThS265yDAfwe97c23

2. OpenAI – Your gateway to cutting-edge artificial intelligence innovation. https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o

3. Microsoft Copilot – Your productivity powerhouse. https://whatsapp.com/channel/0029VbAW0QBDOQIgYcbwBd1l

4. Perplexity AI – Your AI-powered research buddy with real-time answers. https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U

5. Generative AI – Your creative partner for text, images, code, and more. https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U

6. Prompt Engineering – Your secret weapon to get the best out of AI. https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b

7. AI Tools – Your toolkit for automating, analyzing, and accelerating everything. https://whatsapp.com/channel/0029VaojSv9LCoX0gBZUxX3B

8. AI Studio – Everything about AI & Tech https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U

9. Google Gemini – Generate images & videos with AI. https://whatsapp.com/channel/0029Vb5Q4ly3mFY3Jz7qIu3i/103

10. Data Science & Machine Learning – Your fuel for insights, predictions, and smarter decisions. https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

11. Data Science Projects – Your engine for building smarter, self-learning systems. https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z/208

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🚀🔥 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 — 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺
Master the most in-demand AI skill in today’s job market: building autonomous AI systems.

In Ready Tensor’s free, project-first program, you’ll create three portfolio-ready projects using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, and vector databases — and deploy production-ready agents that employers will notice.

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R CHEATSHEET - Part 1

Hi guys,

Now you can directly find job opportunities on WhatsApp. Here is the list of top job related channels on WhatsApp 👇

Latest Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226

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

Software Engineer Jobs: https://whatsapp.com/channel/0029VatL9a22kNFtPtLApJ2L

Data Science Jobs: https://whatsapp.com/channel/0029VaxTMmQADTOA746w7U2P

Data Analyst Jobs: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J

Web Developer Jobs: https://whatsapp.com/channel/0029Vb1raTiDjiOias5ARu2p

Remote Jobs: https://whatsapp.com/channel/0029Vb1RrFuC1Fu3E0aiac2E

Google Jobs: https://whatsapp.com/channel/0029VaxngnVInlqV6xJhDs3m

Hope it helps :)

Complete SQL road map
👇👇

1.Intro to SQL
• Definition
• Purpose
• Relational DBs
• DBMS

2.Basic SQL Syntax
• SELECT
• FROM
• WHERE
• ORDER BY
• GROUP BY

3. Data Types
• Integer
• Floating-Point
• Character
• Date
• VARCHAR
• TEXT
• BLOB
• BOOLEAN

4.Sub languages
• DML
• DDL
• DQL
• DCL
• TCL

5. Data Manipulation
• INSERT
• UPDATE
• DELETE

6. Data Definition
• CREATE
• ALTER
• DROP
• Indexes

7.Query Filtering and Sorting
• WHERE
• AND
• OR Conditions
• Ascending
• Descending

8. Data Aggregation
• SUM
• AVG
• COUNT
• MIN
• MAX

9.Joins and Relationships
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• Self-Joins
• Cross Joins
• FULL OUTER JOIN

10.Subqueries
• Subqueries used in
• Filtering data
• Aggregating data
• Joining tables
• Correlated Subqueries

11.Views
• Creating
• Modifying
• Dropping Views

12.Transactions
• ACID Properties
• COMMIT
• ROLLBACK
• SAVEPOINT
• ROLLBACK TO SAVEPOINT

13.Stored Procedures
• CREATE PROCEDURE
• ALTER PROCEDURE
• DROP PROCEDURE
• EXECUTE PROCEDURE
• User-Defined Functions (UDFs)

14.Triggers
• Trigger Events
• Trigger Execution and Syntax

15. Security and Permissions
• CREATE USER
• GRANT
• REVOKE
• ALTER USER
• DROP USER

16.Optimizations
• Indexing Strategies
• Query Optimization

17.Normalization
• 1NF(Normal Form)
• 2NF
• 3NF
• BCNF

18.Backup and Recovery
• Database Backups
• Point-in-Time Recovery

19.NoSQL Databases
• MongoDB
• Cassandra etc...
• Key differences

20. Data Integrity
• Primary Key
• Foreign Key

21.Advanced SQL Queries
• Window Functions
• Common Table Expressions (CTEs)

22.Full-Text Search
• Full-Text Indexes
• Search Optimization

23. Data Import and Export
• Importing Data
• Exporting Data (CSV, JSON)
• Using SQL Dump Files

24.Database Design
• Entity-Relationship Diagrams
• Normalization Techniques

25.Advanced Indexing
• Composite Indexes
• Covering Indexes

26.Database Transactions
• Savepoints
• Nested Transactions
• Two-Phase Commit Protocol

27.Performance Tuning
• Query Profiling and Analysis
• Query Cache Optimization

------------------ END -------------------

Essential Topics to Master Data Science Interviews: 🚀

SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables

2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries

3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)

Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages

2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets

3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)

Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting

2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)

3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards

Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)

2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX

3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes

Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.

Show some ❤️ if you're ready to elevate your data science game! 📊

ENJOY LEARNING 👍👍

Essential Skills to Master for a Data Analytics Career

1️⃣ SQL 🗂️ Learn how to query databases, use joins, aggregate data, and write optimized SQL queries.

2️⃣ Data Visualization 📊 Communicate insights effectively using tools like Power BI, Tableau, and Excel charts.

3️⃣ Python for Data Analysis 🐍 Use libraries like Pandas, NumPy, and Matplotlib to manipulate and analyze data efficiently.

4️⃣ Statistical Thinking 📈 Understand key concepts like probability, hypothesis testing, and regression analysis for data-driven decisions.

5️⃣ Business Acumen 💼 Know how to translate raw data into actionable insights that drive business growth.

6️⃣ Data Cleaning & Wrangling 🧹 Real-world data is messy—learn techniques to handle missing values, duplicates, and outliers.

7️⃣ Excel Proficiency 📑 Master formulas, PivotTables, and Power Query for quick and effective data analysis.

8️⃣ Communication & Storytelling 🎤 Turn complex data findings into compelling narratives that stakeholders can understand.

9️⃣ Critical Thinking & Problem-Solving 🔍 Go beyond numbers—ask the right questions and identify meaningful patterns in data.

🔟 Continuous Learning & AI Integration 🤖 Stay updated with new analytics trends and leverage AI for automation and insights.

Master these skills, and you’ll be well on your way to becoming a top-tier data analyst! 🚀

Like for detailed explanation ❤️

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Mathematics for Machine Learning

Published by Cambridge University Press (published April 2020)

https://mml-book.com

PDF: https://mml-book.github.io/book/mml-book.pdf

🔎 Gender & Age Detection using Python Machine Learning! 🤖

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Complete Data Science Roadmap
👇👇

1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)

2. Mathematics and Statistics
- Probability and Distributions
- Descriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics

3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD

4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering

5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)

6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation

7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics

8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data

9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)

10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data

11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models

12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)

13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)

14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models

15. Tools for Data Science
- Jupyter, Git, Docker

16. Career Path & Certifications
- Building a Data Science Portfolio

Like if you need similar content 😄👍

Free Notes & Books to learn Data Science: https://t.iss.one/datasciencefree

Python Project Ideas: https://t.iss.one/dsabooks/85

Best Resources to learn Data Science 👇👇

Python Tutorial

Data Science Course by Kaggle

Machine Learning Course by Google

Best Data Science & Machine Learning Resources

Interview Process for Data Science Role at Amazon

Python Interview Resources

Join @free4unow_backup for more free courses

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ENJOY LEARNING👍👍

Few ways to optimise SQL Queries 👇👇

Use Indexing: Properly indexing your database tables can significantly speed up query performance by allowing the database to quickly locate the rows needed for a query.

Optimize Joins: Minimize the number of joins and use appropriate join types (e.g., INNER JOIN, LEFT JOIN) to ensure efficient data retrieval.

Avoid SELECT * : Instead of selecting all columns using SELECT *, explicitly specify only the columns needed for the query to reduce unnecessary data transfer and processing overhead.

Use WHERE Clause Wisely: Filter rows early in the query using WHERE clause to reduce the dataset size before joining or aggregating data.

Avoid Subqueries: Whenever possible, rewrite subqueries as JOINs or use Common Table Expressions (CTEs) for better performance.

Limit the Use of DISTINCT: Minimize the use of DISTINCT as it requires sorting and duplicate removal, which can be resource-intensive for large datasets.

Optimize GROUP BY and ORDER BY: Use GROUP BY and ORDER BY clauses judiciously, and ensure that they are using indexed columns whenever possible to avoid unnecessary sorting.

Consider Partitioning: Partition large tables to distribute data across multiple nodes, which can improve query performance by reducing I/O operations.

Monitor Query Performance: Regularly monitor query performance using tools like query execution plans, database profiler, and performance monitoring tools to identify and address bottlenecks.

Hope it helps :)