Robotics Roadmap
|
| |-- Fundamentals
| |-- Introduction to Robotics
| | |-- What is Robotics?
| | |-- Types of Robots
| | |-- Robotics Applications
|
|-- Robotics Hardware
| |-- Sensors
| | |-- Proximity Sensors
| | |-- Vision Sensors
| | |-- Force Sensors
| |-- Actuators
| | |-- Motors (DC Motors, Servo Motors)
| | |-- Hydraulic and Pneumatic Actuators
| |-- Microcontrollers and Controllers
| | |-- Arduino
| | |-- Raspberry Pi
| | |-- Robot Operating System (ROS)
|
|-- Robotics Programming
| |-- Languages
| | |-- C/C++
| | |-- Python
| | |-- MATLAB
| |-- Robotics Frameworks
| | |-- ROS (Robot Operating System)
| | |-- VEX Robotics
|
|-- Kinematics and Dynamics
| |-- Forward and Inverse Kinematics
| |-- Robot Motion Planning
| |-- Trajectory Generation
|
|-- Robotics Control Systems
| |-- PID Control
| |-- State-Space Control
| |-- Fuzzy Logic Controllers
|
|-- Robotics Perception
| |-- Computer Vision for Robotics
| | |-- Image Processing
| | |-- Object Detection and Tracking
| |-- LiDAR and Depth Sensing
| |-- Sensor Fusion
|
|-- Artificial Intelligence in Robotics
| |-- Machine Learning for Robotics
| | |-- Supervised and Unsupervised Learning
| | |-- Reinforcement Learning for Robotics
| |-- SLAM (Simultaneous Localization and Mapping)
| |-- Path Planning and Navigation
|
|-- Robotics Development Platforms
| |-- Simulation Software
| | |-- Gazebo
| | |-- V-REP
| |-- Hardware Platforms
| | |-- Lego Mindstorms
| | |-- Open Source Robot Kits (TurtleBot, etc.)
|
|-- Robotics Applications
| |-- Industrial Robotics
| | |-- Automation in Manufacturing
| | |-- Pick and Place Robots
| |-- Service Robotics
| | |-- Healthcare Robots
| | |-- Hospitality and Delivery Robots
| |-- Autonomous Vehicles
| | |-- Self-Driving Cars
| | |-- Drones
|
|-- Ethics and Future of Robotics
| |-- Ethical Considerations in Robotics
| |-- Robot-Assisted Labor and Job Displacement
| |-- Future of AI and Robotics

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🔰 Git & GitHub Roadmap for Beginners 2025
├── 🧠 What is Version Control? Why Git?
├── ⚙️ Installing Git & Setting up GitHub
├── 📁 Git Basics (init, clone, add, commit)
├── 🌿 Branching & Merging
├── 🔄 Push, Pull, and Fetch
├── 🧠 Understanding Merge Conflicts
├── 🔧 .gitignore & Git Configurations
├── 🧪 Working with Remotes (origin, upstream)
├── 📦 Tagging & Releases
├── 📜 Git Log, Revert, Reset
├── 🧪 Git & GitHub Projects:
│ ├── Portfolio Versioning
│ ├── Team Collaboration on a Blog Project
│ ├── Open-source Contribution to a Repo

Free Resources to learn Git & GitHub 👇👇

http://GitFluence.com

https://www.tg-me.com/github_coding

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

https://learn.microsoft.com/en-us/training/modules/intro-to-git/

https://docs.github.com/en/get-started/start-your-journey/git-and-github-learning-resources

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Learn Machine Learning & Artificial Intelligence in 12 Weeks

1. Week 1-2: Machine Learning & Deep Learning Basics

1.1 Learn the fundamentals of math and statistics, including linear algebra, probability, and calculus.
1.2 Study Python and essential libraries like NumPy, Pandas, Matplotlib, and Seaborn.
1.3 Understand core machine learning algorithms such as linear regression, decision trees, and SVM.
1.4 Explore deep learning basics, including neural networks, backpropagation, and activation functions.
1.5 Practice by working on ML projects using Scikit-learn and training a simple neural network with TensorFlow or PyTorch.


2. Week 3-4: Deep Learning & Neural Networks

2.1 Learn about convolutional neural networks (CNNs) for image processing.
2.2 Study recurrent neural networks (RNNs) for sequential data and explore LSTMs and GRUs for better text processing.
2.3 Understand autoencoders as a foundation for generative models.
2.4 Implement CNNs for image classification using datasets like MNIST or CIFAR-10.
2.5 Train an RNN for text generation using an LSTM-based model.


3. Week 5-6: Transformers & Attention Mechanism

3.1 Understand the attention mechanism and self-attention, the foundation of transformer models.
3.2 Study transformer architectures such as BERT, GPT, and T5.
3.3 Learn about encoder-decoder architectures and their applications.
3.4 Fine-tune BERT for text classification tasks like sentiment analysis.
3.5 Use GPT models to generate coherent text.


4. Week 7-8: Generative AI & Hugging Face

4.1 Explore generative adversarial networks (GANs) for image generation.
4.2 Study variational autoencoders (VAEs) and diffusion models like Stable Diffusion and DALL·E.
4.3 Learn to use Hugging Face for accessing and fine-tuning pre-trained models.
4.4 Generate images using Stable Diffusion.
4.5 Build a chatbot using GPT models.


5. Week 9-10: APIs, Deployment & Real-World Applications

5.1 Learn to integrate AI models using APIs from OpenAI and Hugging Face.
5.2 Work with LangChain for AI agents and chatbot development.
5.3 Explore deployment techniques using Flask, FastAPI, or Gradio.
5.4 Optimize AI models for web and mobile applications.
5.5 Build and deploy real-world projects such as chatbots, PDF or video summarizers, or AI-powered image generators.


6. Week 11-12: Showcase & Stay Updated

6.1 Push projects to GitHub and Kaggle to build a strong portfolio.
6.2 Share knowledge through technical blogs on Medium or LinkedIn.
6.3 Stay updated by following AI research papers, news sources, and communities on Twitter and Reddit.
6.4 Learn about MLOps and model optimization for production-level AI deployment.
6.5 Continue experimenting with new models and stay engaged with the AI community.

Best Resources to learn AI & Machine Learning 👇👇

Learn Python for Free

Prompt Engineering Course

Prompt Engineering Guide

Data Science Course

Google Cloud Generative AI Path

Machine Learning with Python Free Course

Machine Learning Free Book

Deep Learning Nanodegree Program with Real-world Projects

AI, Machine Learning and Deep Learning

Work on projects that align with your interests. Stay curious and experiment with the latest AI models.

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#artificialintelligence

Step-by-Step Approach to Learn AI Agents

➊ Understand What AI Agents Are → Autonomous systems that can perceive, reason, and act
↓
➋ Master the Basics → Python, Data Structures, APIs, and JSON handling
↓
➌ Explore LLMs as Agents → Understand how GPT, Claude, or Gemini can act as reasoning agents
↓
➍ Tool Use & Function Calling → Learn how agents use tools, call APIs, and perform tasks dynamically
↓
➎ Agent Frameworks →
LangChain: For chaining LLM calls and memory
AutoGen / Autogen Studio: For multi-agent collaboration
Haystack: For document question answering
↓
➏ Memory & Persistence → Vector databases (e.g., FAISS, Chroma, Pinecone) for long-term memory
↓
➐ Planning & Reasoning → ReAct, CoT (Chain-of-Thought), and Tree of Thought prompting
↓
➑ Build & Deploy AI Agents →
Personal assistants
Customer support bots
Research agents
Coding copilots

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Glad to see the amazing response

I have curated the following free resources to learn AI Agents:

https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/

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https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E

https://github.com/microsoft/ai-agents-for-beginners

https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/

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Game Development Roadmap
|
| |-- Fundamentals
| |-- Introduction to Game Development
| | |-- Types of Games (2D, 3D, Mobile, VR)
| | |-- Game Development Life Cycle
| |-- Game Design Basics
| | |-- Game Mechanics
| | |-- Storytelling in Games
| | |-- User Experience (UX) in Games
|
|-- Game Engines
| |-- Unity
| | |-- Understanding the Interface
| | |-- Scripting with C#
| | |-- Physics and Animation
| |-- Unreal Engine
| | |-- Understanding Blueprints
| | |-- C++ Programming
| |-- Godot
| | |-- Game Scene Development
| | |-- GDScript Basics
|
|-- Game Programming
| |-- Programming Languages
| | |-- C++ (Unreal Engine)
| | |-- C# (Unity)
| | |-- Python (Prototyping and Scripting)
| |-- Game Loop and Event Handling
| | |-- Game State Management
| | |-- Input Handling (Keyboard, Mouse, Touch, etc.)
|
|-- 2D Game Development
| |-- Game Mechanics for 2D
| | |-- Collision Detection
| | |-- Physics Simulation
| | |-- Movement and Animation
| |-- Graphics and Art
| | |-- Sprite Animation
| | |-- Tile-based Level Design
|
|-- 3D Game Development
| |-- 3D Modeling and Animation
| | |-- Using Blender for 3D Assets
| | |-- Rigging and Skinning
| |-- 3D Physics
| | |-- Rigidbody Dynamics
| | |-- Collisions in 3D Space
| |-- Camera Systems
| | |-- First-person and Third-person Cameras
| | |-- Camera Interpolation and Smoothness
|
|-- Game Audio
| |-- Sound Effects
| | |-- Creating and Implementing SFX
| | |-- Audio Design in Games
| |-- Background Music
| | |-- Dynamic Soundtracks
| | |-- Adaptive Music for Game Events
|
|-- Artificial Intelligence in Games
| |-- AI for NPCs
| | |-- Pathfinding (A* Algorithm)
| | |-- State Machines and Behavior Trees
| |-- Procedural Content Generation
| |-- AI for Opponents
| | |-- Combat AI
| | |-- Strategy AI
|
|-- Multiplayer Game Development
| |-- Networking Basics
| | |-- Client-Server Architecture
| | |-- Real-time Multiplayer Games
| |-- Matchmaking and Server Architecture
| | |-- Peer-to-peer Networking
| | |-- Dedicated Servers
|
|-- Game Monetization
| |-- In-App Purchases
| | |-- Virtual Goods and Currency
| | |-- Ad Integration
| |-- Game Distribution
| | |-- Steam, Epic Games Store, and App Stores
| | |-- Publishing and Marketing Strategies
|
|-- Virtual Reality (VR) and Augmented Reality (AR) Development
| |-- VR/AR Basics
| | |-- Oculus Rift and HTC Vive Development
| | |-- Unity/Unreal for VR/AR
| |-- Interaction Design for VR/AR
| | |-- Hand Tracking and Gesture Recognition
| | |-- Immersive Environment Design
|
|-- Game Testing and Debugging
| |-- Quality Assurance (QA)
| | |-- Playtesting and Feedback
| | |-- Bug Tracking and Fixing
| |-- Performance Optimization
| | |-- Reducing Load Times and Memory Usage
| | |-- Frame Rate Optimization
|
|-- Game Publishing and Marketing
| |-- Game Release Strategies
| | |-- Early Access and Beta Testing
| | |-- Launch Planning and Publicity
| |-- Community Engagement
| | |-- Building a Player Community
| | |-- Social Media and Influencer Marketing

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🌟 Step-by-Step Guide to Become a Python Developer in 2025 🌟

1. Master the Basics

🐍 Python Syntax & Data Types: Learn variables, strings, lists, tuples, dictionaries, and more.

🔁 Control Flow: Use if, else, for, and while like a logic ninja.


2. Work with Functions & Modules

🧠 Break your code into reusable chunks with functions.

📦 Explore Python’s rich standard library for powerful built-in tools.


3. Object-Oriented Programming (OOP)

🧱 Learn about Classes and Objects: Build scalable, reusable code like a pro architect.

♻️ Understand inheritance, polymorphism, encapsulation, and abstraction.


4. Explore Popular Python Libraries

📊 Pandas, NumPy for data analysis.

📈 Matplotlib, Seaborn for visualizations.

🧪 Requests, BeautifulSoup, Selenium for web scraping.


5. Database Interaction

🗄 Connect Python to databases using SQLite, MySQL, or PostgreSQL.

🔍 Learn how to read, write, and manipulate data.


6. Version Control & Collaboration

🔄 Master Git & GitHub: Collaborate, manage code history, and work on real-world projects.


7. Web Development with Python

🌐 Build web apps using Flask or Django.

✨ Learn about routing, templates, and backend logic.


8. Automate Everything!

🤖 Write scripts to automate boring tasks: Rename files, send emails, scrape websites, etc.

🗓 Use Python for scheduling and workflow efficiency.


9. Testing and Debugging

🧪 Learn unit testing with unittest or pytest.

🐞 Become a debugging wizard using breakpoints and pdb.


10. Build Real Projects

🏗 Start small: To-Do apps, calculators, web scrapers.

🚀 Level up: Build dashboards, chatbots, or portfolio websites.


11. Specialize

🧠 Go into Data Science, Web Development, Automation, Machine Learning, or APIs — based on what excites you most.


12. Network and Grow

🤝 Join dev communities: Discord, Reddit, GitHub, LinkedIn.

💬 Participate in hackathons, open source, or blog your learnings.

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Useful websites to practice and enhance your data analytics skills
👇👇

1. Python
http://learnpython.org

2. SQL
https://www.sql-practice.com/

3. Excel
https://excel-practice-online.com/

4. Power BI
https://www.workout-wednesday.com/power-bi-challenges/

5. Quiz and Interview Questions
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Haven't shared lot of resources to avoid too much distraction

Just focus on the basics, practice learnings and work on building projects  to improve your skills.

Thats the best way to learn in my opinion 😄

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Free Courses/ Certifications for a full-stack career in 2025:

1. Web design https://freecodecamp.org/learn/2022/responsive-web-design/

2. JavaScript https://freecodecamp.org/learn/javascript-algorithms-and-data-structures/

https://bit.ly/3PI8JWB

3. Front-end libraries https://freecodecamp.org/learn/front-end-development-libraries/

5. Back-end and APIs
https://freecodecamp.org/learn/back-end-development-and-apis/

6. Databases
https://freecodecamp.org/learn/relational-database/

7. Full Stack Web Development Bootcamp
https://www.tg-me.com/webdevcoursefree/461

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Learn for free ✅

HTML — https://www.w3schools.com/html
CSS — http://CSS-tricks.com
JavaScript — http://LearnJavaScript.online
DSA --- https://www.tg-me.com/dsabooks/21
Git, GitHub -- http://LearnGitBranching.js.org
React — http://React-tutorial.app
API — http://RapidAPI.com/comics
SQL — http://SQLbolt.com
Python -- https://www.tg-me.com/pythondevelopersindia/76
Data Science -- https://www.tg-me.com/datalemur
PHP --- https://bit.ly/3QkY3wW
ML -- https://developers.google.com/machine-learning/crash-course
ChatGPT -- https://www.tg-me.com/aisigma
AI -- http://microsoft.github.io/AI-For-Beginners

ENJOY LEARNING 👍👍

Complete Python Programming Roadmap for Beginners

Stage 1: Python Fundamentals (Week 1–2)

Goals:

- Understand basic syntax and structure.

- Get comfortable with writing and running Python code.


Topics:

- Variables, Data Types (int, float, str, bool)

- Input/Output

- Operators

- If-Else Conditions

- Loops (for, while)

- Basic Functions


Practice Platforms:

- W3Schools Python

- Replit

- Python Exercises

Stage 2: Data Structures in Python (Week 3–4)

Goals:

- Learn to store and manipulate data efficiently.


Topics:

- Lists, Tuples

- Sets, Dictionaries

- String manipulation

- List comprehensions


Mini Projects:

- Word counter

- Contact book using dictionary


Practice:

- HackerRank

- LeetCode Easy Python Problems

Stage 3: Functions, Error Handling & File Handling (Week 5–6)

Goals:

- Write reusable code.

- Learn to debug and handle exceptions.


Topics:

- Creating & calling functions

- *args and **kwargs

- Try-Except blocks

- Reading/writing files (.txt, .csv)

- Working with with open(...)


Mini Projects:

- Quiz app

- File-based To-Do list

Stage 4: Modules, Libraries & OOP Basics (Week 7–8)

Goals:

- Understand Object-Oriented Programming and use Python libraries.


Topics:

- Importing and using libraries

- Creating your own modules

- Classes & Objects

- init, methods, inheritance


Mini Projects:

- Calculator using OOP

- Basic Library System


Practice:

- Real Python OOP Guide

Stage 5: First Real Projects (Week 9–10)

Goals:

- Apply your knowledge to build end-to-end mini projects.

Ideas:

- Weather App using API

- Simple Expense Tracker

- Rock-Paper-Scissors Game

- Number Guessing Game with levels


Bonus Tips:

- Keep a GitHub Repo to track progress.

- Ask questions on forums like Stack Overflow or Reddit’s r/learnpython.

- Use ChatGPT to get code explanations or help debugging.

- Schedule 1 hour daily for coding + 30 mins for review.


Best Resource to learn Python

Python Interview Questions with Answers

Freecodecamp Python Course with FREE Certificate

Python for Data Analysis and Visualization

Python course for beginners by Microsoft

Python course by Google

Python Coding Challenge

Machine Learning with Python

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Complete Data Analytics Mastery: From Basics to Advanced 🚀

Begin your Data Analytics journey by mastering the fundamentals:
- Understanding Data Types and Formats
- Basics of Exploratory Data Analysis (EDA)
- Introduction to Data Cleaning Techniques
- Statistical Foundations for Data Analytics
- Data Visualization Essentials

Grasp these essentials in just a week to build a solid foundation in data analytics.

Once you're comfortable, dive into intermediate topics:
- Advanced Data Visualization (using tools like Tableau)
- Hypothesis Testing and A/B Testing
- Regression Analysis
- Time Series Analysis for Analytics
- SQL for Data Analytics

Take another week to solidify these skills and enhance your ability to draw meaningful insights from data.

Ready for the advanced level? Explore cutting-edge concepts:
- Machine Learning for Data Analytics
- Predictive Analytics
- Big Data Analytics (Hadoop, Spark)
- Advanced Statistical Methods (Multivariate Analysis)
- Data Ethics and Privacy in Analytics

These advanced concepts can be mastered in a couple of weeks with focused study and practice.

Remember, mastery comes with hands-on experience:
- Work on a simple data analytics project
- Tackle an intermediate-level analysis task
- Challenge yourself with an advanced analytics project involving real-world data sets

Consistent practice and application of analytics techniques are the keys to becoming a data analytics pro.

Best platforms to learn:
- Intro to Data Analysis
- Intro to Data Visualisation
- SQL courses with Certificate
- Freecodecamp Python Course
- 365DataScience
- Data Analyst Interview Questions
- Free SQL Resources

Share your progress and insights with others in the data analytics community. Enjoy the fascinating journey into the realm of data analytics! 👩‍💻👨‍💻

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When people thank me saying my telegram channel helped them a lot in learning new things, first question I ask them is which channel 😂

I have created multiple telegram channels but this one is my favourite.

Data Science Detailed Roadmap
|
| | |-- Fundamentals
| |-- Introduction to Data Science
| | |-- What is Data Science?
| | |-- Roles: Analyst vs Scientist vs Engineer
| | |-- Data Science Workflow
| |-- Math and Statistics
| | |-- Descriptive & Inferential Statistics
| | |-- Probability Theory
| | |-- Linear Algebra & Calculus Basics
| |-- Programming for Data Science
| |-- Python
| | |-- Variables, Loops, Functions
| | |-- NumPy, Pandas, Matplotlib, Seaborn
| |-- R Programming (Optional but Useful)
| | |-- Data Manipulation with dplyr, tidyr
| | |-- Visualization with ggplot2
| |-- SQL
| | |-- SELECT, WHERE, GROUP BY, JOINS
| | |-- Subqueries and Window Functions
| |-- Data Wrangling & Preprocessing
| |-- Cleaning and Handling Missing Data
| |-- Data Transformation & Encoding
| |-- Feature Engineering
| |-- Working with APIs and Web Scraping
| |-- Data Visualization
| |-- Exploratory Data Analysis (EDA)
| |-- Visualization Tools
| | |-- Python: Seaborn, Plotly
| | |-- BI Tools: Power BI, Tableau
| |-- Machine Learning
| |-- Supervised Learning
| | |-- Linear Regression
| | |-- Classification (Logistic Regression, Decision Trees, SVM)
| |-- Unsupervised Learning
| | |-- Clustering (K-Means, DBSCAN)
| | |-- Dimensionality Reduction (PCA, t-SNE)
| |-- Model Evaluation
| | |-- Cross-validation, Confusion Matrix
| | |-- ROC-AUC, Precision, Recall, F1 Score
| |-- Deep Learning & Neural Networks
| |-- Introduction to Neural Networks
| |-- Frameworks: TensorFlow, Keras, PyTorch
| |-- CNNs for Image Data
| |-- RNNs & LSTMs for Time Series / Text
| |-- Projects & Real-World Applications
| |-- End-to-End ML Projects
| |-- Kaggle Competitions
| |-- Case Studies (Retail, Finance, Healthcare)
| |-- Big Data & Cloud Tools | |-- Introduction to Big Data
| | |-- Hadoop, Spark
| |-- Cloud Platforms
| | |-- AWS, GCP, Azure (S3, EC2, BigQuery, SageMaker)
| |-- Data Engineering Basics
| |-- ETL Pipelines
| |-- Workflow Automation with Airflow
| |-- Data Warehousing (Snowflake, Redshift)
| |-- Natural Language Processing (NLP)
| |-- Text Preprocessing
| |-- Bag of Words, TF-IDF
| |-- NLP Libraries (NLTK, spaCy)
| |-- Transformers (BERT, GPT)
| |-- Time Series Analysis
| |-- Trends, Seasonality, Forecasting
| |-- ARIMA, Prophet
| |-- LSTM for Time Series
| |-- Model Deployment
| |-- Building Web Apps (Streamlit, Flask)
| |-- Model Serialization (Pickle, joblib)
| |-- Deploy to Cloud (Heroku, AWS, GCP)
| |-- Soft Skills & Career Prep
| |-- Resume Projects and Portfolio
| |-- Git and GitHub for Version Control
| |-- Interview Preparation
| |-- Communication & Storytelling with Data
| |-- Bonus Topics
| |-- Reinforcement Learning Basics
| |-- Ethics in AI & Data Privacy
| |-- MLOps and CI/CD for Data Science
| |-- Community & Growth
| |-- Kaggle, GitHub, LinkedIn
| |-- Contributing to Open Source
| |-- Blogging / Sharing Your Learnings

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Python Free Course

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Google Cloud Generative AI Path

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Essential Python Libraries to build your career in Data Science 📊👇

1. NumPy:
- Efficient numerical operations and array manipulation.

2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).

3. Matplotlib:
- 2D plotting library for creating visualizations.

4. Seaborn:
- Statistical data visualization built on top of Matplotlib.

5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.

6. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.

7. PyTorch:
- Deep learning library, particularly popular for neural network research.

8. SciPy:
- Library for scientific and technical computing.

9. Statsmodels:
- Statistical modeling and econometrics in Python.

10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).

11. Gensim:
- Topic modeling and document similarity analysis.

12. Keras:
- High-level neural networks API, running on top of TensorFlow.

13. Plotly:
- Interactive graphing library for making interactive plots.

14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.

15. OpenCV:
- Library for computer vision tasks.

As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.

Free Notes & Books to learn Data Science: https://www.tg-me.com/datasciencefree

Python Project Ideas: https://www.tg-me.com/dsabooks/85

Best Resources to learn Python & 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

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Why Join
Level up your skills, add a strong line to your resume, tackle pro-level tasks, compete for an award, and get an opportunity to showcase your work at AI Journey, a leading international AI conference.

How to Join
1. Register here.
2. Choose your track.
3. Create your solution and submit it by 30 October 2025.

🚀 Ready for a challenge? Join a global developer community and show your AI skills!

Web Development Mastery: From Basics to Advanced 🚀

Start with the fundamentals:
- HTML
- CSS
- JavaScript
- Responsive Design
- Basic DOM Manipulation
- Git and Version Control

You can grasp these essentials in just a week.

Once you're comfortable, dive into intermediate topics:
- AJAX
- APIs
- Frameworks like React, Angular, or Vue
- Front-end Build Tools (Webpack, Babel)
- Back-end basics with Node.js, Express, or Django

Take another week to solidify these skills.

Ready for the advanced level? Explore:
- Authentication and Authorization
- RESTful APIs
- GraphQL
- WebSockets
- Docker and Containerization
- Testing (Unit, Integration, E2E)

These advanced concepts can be mastered in a couple of weeks.

Remember, mastery comes with practice:
- Create a simple web project
- Tackle an intermediate-level project
- Challenge yourself with an advanced project involving complex features

Consistent practice is the key to becoming a web development pro.

Best platforms to learn:
- FreeCodeCamp
- Web Development Free Courses
- Web Development Roadmap
- Projects

Share your progress and learnings with others in the community. Enjoy the journey! 👩‍💻👨‍💻

Join @free4unow_backup for more free resources.

Like this post if it helps 😄❤️

ENJOY LEARNING 👍👍

30-day Roadmap plan for SQL covers beginner, intermediate, and advanced topics 👇

Week 1: Beginner Level

Day 1-3: Introduction and Setup
1. Day 1: Introduction to SQL, its importance, and various database systems.
2. Day 2: Installing a SQL database (e.g., MySQL, PostgreSQL).
3. Day 3: Setting up a sample database and practicing basic commands.

Day 4-7: Basic SQL Queries
4. Day 4: SELECT statement, retrieving data from a single table.
5. Day 5: WHERE clause and filtering data.
6. Day 6: Sorting data with ORDER BY.
7. Day 7: Aggregating data with GROUP BY and using aggregate functions (COUNT, SUM, AVG).

Week 2-3: Intermediate Level

Day 8-14: Working with Multiple Tables
8. Day 8: Introduction to JOIN operations.
9. Day 9: INNER JOIN and LEFT JOIN.
10. Day 10: RIGHT JOIN and FULL JOIN.
11. Day 11: Subqueries and correlated subqueries.
12. Day 12: Creating and modifying tables with CREATE, ALTER, and DROP.
13. Day 13: INSERT, UPDATE, and DELETE statements.
14. Day 14: Understanding indexes and optimizing queries.

Day 15-21: Data Manipulation
15. Day 15: CASE statements for conditional logic.
16. Day 16: Using UNION and UNION ALL.
17. Day 17: Data type conversions (CAST and CONVERT).
18. Day 18: Working with date and time functions.
19. Day 19: String manipulation functions.
20. Day 20: Error handling with TRY...CATCH.
21. Day 21: Practice complex queries and data manipulation tasks.

Week 4: Advanced Level

Day 22-28: Advanced Topics
22. Day 22: Working with Views.
23. Day 23: Stored Procedures and Functions.
24. Day 24: Triggers and transactions.
25. Day 25: Windows Function

Day 26-30: Real-World Projects
26. Day 26: SQL Project-1
27. Day 27: SQL Project-2
28. Day 28: SQL Project-3
29. Day 29: Practice questions set
30. Day 30: Final review and practice, explore advanced topics in depth, or work on a personal project.

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Free Resources to learn SQL: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1394

Data Analytics Roadmap
|
|-- Fundamentals
|   |-- Mathematics
|   |   |-- Descriptive Statistics
|   |   |-- Inferential Statistics
|   |   |-- Probability Theory
|   |
|   |-- Programming
|   |   |-- Python (Focus on Libraries like Pandas, NumPy)
|   |   |-- R (For Statistical Analysis)
|   |   |-- SQL (For Data Extraction)
|
|-- Data Collection and Storage
|   |-- Data Sources
|   |   |-- APIs
|   |   |-- Web Scraping
|   |   |-- Databases
|   |
|   |-- Data Storage
|   |   |-- Relational Databases (MySQL, PostgreSQL)
|   |   |-- NoSQL Databases (MongoDB, Cassandra)
|   |   |-- Data Lakes and Warehousing (Snowflake, Redshift)
|
|-- Data Cleaning and Preparation
|   |-- Handling Missing Data
|   |-- Data Transformation
|   |-- Data Normalization and Standardization
|   |-- Outlier Detection
|
|-- Exploratory Data Analysis (EDA)
|   |-- Data Visualization Tools
|   |   |-- Matplotlib
|   |   |-- Seaborn
|   |   |-- ggplot2
|   |
|   |-- Identifying Trends and Patterns
|   |-- Correlation Analysis
|
|-- Advanced Analytics
|   |-- Predictive Analytics (Regression, Forecasting)
|   |-- Prescriptive Analytics (Optimization Models)
|   |-- Segmentation (Clustering Techniques)
|   |-- Sentiment Analysis (Text Data)
|
|-- Data Visualization and Reporting
|   |-- Visualization Tools
|   |   |-- Power BI
|   |   |-- Tableau
|   |   |-- Google Data Studio
|   |
|   |-- Dashboard Design
|   |-- Interactive Visualizations
|   |-- Storytelling with Data
|
|-- Business Intelligence (BI)
|   |-- KPI Design and Implementation
|   |-- Decision-Making Frameworks
|   |-- Industry-Specific Use Cases (Finance, Marketing, HR)
|
|-- Big Data Analytics
|   |-- Tools and Frameworks
|   |   |-- Hadoop
|   |   |-- Apache Spark
|   |
|   |-- Real-Time Data Processing
|   |-- Stream Analytics (Kafka, Flink)
|
|-- Domain Knowledge
|   |-- Industry Applications
|   |   |-- E-commerce
|   |   |-- Healthcare
|   |   |-- Supply Chain
|
|-- Ethical Data Usage
|   |-- Data Privacy Regulations (GDPR, CCPA)
|   |-- Bias Mitigation in Analysis
|   |-- Transparency in Reporting

Free Resources to learn Data Analytics skills👇👇

1. SQL

https://mode.com/sql-tutorial/introduction-to-sql

https://www.tg-me.com/sqlspecialist/738

2. Python

https://www.learnpython.org/

https://www.tg-me.com/pythondevelopersindia/873

https://bit.ly/3T7y4ta

https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial

3. R

https://datacamp.pxf.io/vPyB4L

4. Data Structures

https://leetcode.com/study-plan/data-structure/

5. Data Visualization

https://www.freecodecamp.org/learn/data-visualization/

https://www.tg-me.com/Data_Visual/2

https://www.tableau.com/learn/training/20223

https://www.workout-wednesday.com/power-bi-challenges/

6. Excel

https://excel-practice-online.com/

https://www.tg-me.com/excel_data

https://www.w3schools.com/EXCEL/index.php

Join @free4unow_backup for more free courses

Like for more ❤️

ENJOY LEARNING 👍👍