Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert

🔥 FREE Resources to Learn Web Development 🌐

🔹 freeCodeCamp – HTML, CSS, JavaScript, React & more
👉 https://www.freecodecamp.org/learn/2022/responsive-web-design/

👉https://www.freecodecamp.org/learn/javascript-algorithms-and-data-structures-v8/

🔹 MDN Web Docs – HTML, CSS, JavaScript & Web APIs
👉 https://developer.mozilla.org/en-US/docs/Learn_web_development

🔹 The Odin Project – Full-Stack Web Development
👉 https://www.theodinproject.com/

🔹 CS50 Web – Harvard – Web Programming
👉 https://cs50.harvard.edu/web/

🔹 Full Stack Open – University of Helsinki – React, Node.js & APIs
👉 https://fullstackopen.com/

🔹 JavaScript.info – Modern JavaScript
👉 https://javascript.info/

🔹 W3Schools – HTML, CSS, JavaScript & Web Development
👉 https://www.w3schools.com/whatis

🔹 Web Development Roadmap
👉 https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z/1700

🔹 Frontend Mentor – Frontend Projects
👉 https://www.frontendmentor.io/

🔹 Roadmap.sh – Web Development Roadmaps
👉 https://roadmap.sh/full-stack

🔹 React – Official Documentation
👉 https://react.dev/

🔹 Node.js – Official Documentation
👉 https://nodejs.org/en/learn

🔹 GitHub Skills – Git & GitHub
👉 https://skills.github.com/

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🔥 FREE Resources to Learn Generative AI 🤖

🔹 Generative AI – Google
👉 https://www.cloudskillsboost.google/paths/118

🔹 Generative AI for Everyone – DeepLearning.AI
👉 https://www.deeplearning.ai/courses/generative-ai-for-everyone/

🔹 LLM Course – Hugging Face
👉 https://huggingface.co/learn/llm-course

🔹 Microsoft Generative AI
👉 https://learn.microsoft.com/en-us/training/generative-ai/

🔹 Generative AI Learning Path – AWS
👉 https://aws.amazon.com/training/learn-about/generative-ai/

🔹 Google Generative AI
👉 https://ai.google/

🔹 OpenAI Academy
👉 https://academy.openai.com/

🔹 IBM Generative AI
👉 https://www.ibm.com/training/generative-ai

🔹 NVIDIA Generative AI
👉 https://www.nvidia.com/en-us/learn/generative-ai/

🔹 Generative AI Channel

👉 https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U

🔹 LangChain Academy
👉 https://academy.langchain.com/

🔹 LlamaIndex
👉 https://docs.llamaindex.ai/

🔹 Anthropic Courses
👉 https://www.anthropic.com/learn

❤️ Double Tap For More AI Resources

GigaChat 3.5 Reasoning is a new open-source LLM designed to reason before generating responses. The model breaks problems into stages, builds execution plans, checks intermediate results, and self-corrects when needed.

Built on GigaChat 3.5 Ultra, it was trained on math and coding tasks using multiple step-by-step reasoning paths. An automated verification step reinforces the paths that lead to correct answers, enabling the model to plan multi-step actions, decide when to call external tools, and revise earlier steps independently.

The model uses a proprietary linear attention architecture, which improves efficiency on long contexts by retaining key processed points rather than re-matching queries against the entire prior text.

On math problems, GigaChat 3.5 Reasoning uses on average 37% fewer tokens than DeepSeek V4 Flash Preview. Benchmark gains over the non-reasoning version:
• IFBench: 44 → 77
• Natural Plan: 64 → 80
• LiveCodeBench v6: 56 → 85

The model is open-sourced under the MIT license. Weights are available on Hugging Face:  fp8 | bf16

🔥 FREE Resources to Learn AI 🤖

🔹 Agentic AI – DeepLearning.AI
👉 https://www.deeplearning.ai/courses/agentic-ai

🔹 Google Machine Learning – ML & GenAI
👉 https://developers.google.com/machine-learning

🔹 Hugging Face Learn – LLMs, NLP & AI Agents
👉 https://huggingface.co/learn

🔹 Kaggle Learn – ML, GenAI & Data Science
👉 https://www.kaggle.com/learn

🔹 fast.ai – Practical Deep Learning
👉 https://course.fast.ai/

🔹 MIT OpenCourseWare – AI & Machine Learning
👉 https://ocw.mit.edu/search/?q=artificial+intelligence

🔹 CS50 AI – Harvard
👉 https://cs50.harvard.edu/ai/

🔹 Stanford Online – AI & Machine Learning
👉 https://online.stanford.edu/courses

🔹 NVIDIA Deep Learning Institute – AI & Deep Learning
👉 https://www.nvidia.com/en-us/training/

🔹 TensorFlow Learn – ML & Deep Learning
👉 https://www.tensorflow.org/learn

🔹 PyTorch Learn – Deep Learning
👉 https://docs.pytorch.org/tutorials/

🔹 AI Resources
👉 https://shorturl.at/qHGHX

🔹 Microsoft Learn – AI & Generative AI
👉 https://learn.microsoft.com/ai/

🔹 AWS Skill Builder – AI & Machine Learning
👉 https://skillbuilder.aws/

🔹 IBM SkillsBuild – AI & Generative AI
👉 https://skillsbuild.org/

❤️ Double Tap For More Useful Resources

FREE Resources to Learn Machine Learning 🔥

* Python – python.org/doc
* Math & Stats – khanacademy.org/math
* ML Crash Course – developers.google.com/machine‑learning/crash‑course
* Scikit‑learn – https://scikit-learn.org/
* Pandas – pandas.pydata.org/docs
* Matplotlib – matplotlib.org
* Seaborn – seaborn.pydata.org
* Kaggle Learn (ML) – https://www.kaggle.com/learn/intro-to-machine-learning
* Google ML Guides – developers.google.com/machine‑learning
* TensorFlow Learn – https://www.tensorflow.org/resources/learn-ml
* Fast.ai – fast.ai
* GitHub ML List – github.com/RDelg/machine_learning_resources

Double Tap ♥️ For More

✅ Top Tech Career Paths to Explore in 2026 💻🚀

1. Software Developer
Builds websites, apps, and systems. Needs skills in JavaScript, Python, Java, or C#. Frontend, backend, or full-stack.

2. Cloud Engineer
Works with AWS, Azure, or GCP to manage scalable cloud infrastructure, services, and deployments.

3. DevOps Engineer
Bridges development and operations. Manages CI/CD, automation, monitoring, and infrastructure as code (e.g., Docker, Kubernetes).

4. Cybersecurity Analyst
Protects systems from digital threats. Works on firewalls, threat detection, penetration testing, and data protection.

5. Data Analyst
Turns raw data into insights using SQL, Excel, Python, Tableau, or Power BI. Often a gateway to data science.

6. Blockchain Developer
Builds decentralized apps and smart contracts using Solidity, Ethereum, or other Web3 platforms.

7. AI/ML Engineer
Creates models that learn from data. Requires strong math, Python, ML frameworks (TensorFlow, PyTorch), and real-world deployment skills.

8. UI/UX Designer
Designs seamless user interfaces and experiences. Tools: Figma, Adobe XD, Webflow. Focuses on usability and accessibility.

9. Mobile App Developer
Specializes in Android (Kotlin/Java) or iOS (Swift), or cross-platform tools like Flutter or React Native.

10. Tech Product Manager
Drives product vision, user needs, and team coordination. Requires a mix of tech knowledge, strategy, and communication.

💬 Double Tap ❤️ For More!

Machine Learning Roadmap
| |-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability
| | |-- Statistics
| |
| |-- Programming
| | |-- Python
| | | |-- NumPy
| | | |-- Pandas
| | | |-- Matplotlib
| | |-- R
| |-- Data Handling
| |-- Data Collection
| | |-- APIs
| | |-- Web Scraping
| | |-- SQL Databases
| |
| |-- Data Preparation
| | |-- Cleaning
| | |-- Feature Engineering
| | |-- Encoding
| | |-- Scaling
| |-- Exploratory Data Analysis (EDA)
| |-- Visual Analysis
| | |-- Matplotlib
| | |-- Seaborn
| | |-- Plotly
| |
| |-- Statistical Analysis
| | |-- Correlation
| | |-- Hypothesis Testing
| |-- Core Machine Learning
| |-- Supervised Learning
| | |-- Regression
| | |-- Classification
| | |-- Time Series
| |
| |-- Unsupervised Learning
| | |-- Clustering
| | |-- Dimensionality Reduction
| |
| |-- Model Evaluation
| | |-- Cross Validation
| | |-- Metrics (Accuracy, F1, RMSE)
| |-- Advanced Machine Learning
| |-- Ensemble Models
| | |-- Random Forest
| | |-- XGBoost
| |
| |-- Deep Learning
| | |-- Neural Networks
| | |-- CNN
| | |-- RNN
| | |-- LSTM
| | |-- Transformers
| |
| |-- NLP
| | |-- Text Preprocessing
| | |-- Embeddings
| | |-- Sentiment Analysis
| | |-- LLMs
| |-- Model Deployment
| |-- Flask
| |-- FastAPI
| |-- Streamlit
| |-- Docker
| |-- CI/CD
| |-- MLOps
| |-- Experiment Tracking (MLflow)
| |-- Model Monitoring
| |-- Data Pipelines
| |-- Big Data for ML
| |-- Spark ML
| |-- Hadoop
| |-- Domain Knowledge
| |-- Finance
| |-- Healthcare
| |-- Retail
| |-- Responsible AI
| |-- Bias Detection
| |-- Explainability (SHAP, LIME)
| |-- Privacy and Fairness


Free Resources to Learn Machine Learning👇👇

1. Machine Learning Basics

https://www.kaggle.com/learn/intro-to-machine-learning

https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O

https://www.youtube.com/watch?v=NWONeJKn6kc

2. Python for ML

https://www.kaggle.com/learn/python

https://whatsapp.com/channel/0029VbC0Xa411ulRe5pNJK3E

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

3. Mathematics for ML

https://www.khanacademy.org/math/statistics-probability

https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O

https://www.3blue1brown.com/topics/linear-algebra

4. Data Preprocessing

https://www.kaggle.com/learn/data-cleaning

https://www.kaggle.com/learn/pandas

5. Deep Learning

https://www.deeplearning.ai

https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t

https://www.youtube.com/watch?v=aircAruvnKk

https://www.kaggle.com/learn/intro-to-deep-learning

6. NLP

https://www.kaggle.com/learn/nlp

7. ML Projects

https://www.kaggle.com/competitions

https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z

https://machinelearningmastery.com/start-here/

8. Model Deployment

https://docs.streamlit.io/

https://fastapi.tiangolo.com/

https://www.youtube.com/watch?v=Qw9zlE3t8Ko

❤️ Like for more free resources

Important Topics You Should Know to Learn Web Development:

👉 Beginner Topics

HTML
• Structure of a web page (doctype, html, head, body)
• Headings, paragraphs, lists, links, images
• Forms and input elements
• Semantic tags (header, footer, article, section)

CSS
• Selectors, classes, IDs
• Box model (margin, border, padding, content)
• Flexbox and Grid layout
• Colors, fonts, backgrounds
• Pseudo-classes and pseudo-elements (:hover, :before, :after)

JavaScript (Basics)
• Variables, data types, operators
• Loops, conditions (if, switch)
• Functions and scope
• DOM manipulation (getElementById, querySelector, innerHTML)
• Event handling (onclick, onmouseover)

Version Control
• Git basics: init, clone, commit, push, pull
• Branching and merging

Web Basics
• HTTP/HTTPS, URLs, status codes
• Client vs Server
• Basics of browsers and developer tools

👉 Intermediate Topics

Advanced JavaScript
• ES6+ features (let/const, arrow functions, template literals, destructuring)
• Arrays  objects (map, filter, reduce)
• Promises, async/await, fetch API
• Local storage, session storage, cookies

CSS Advanced
• Animations and transitions
• Media queries and responsive design
• CSS variables
• Preprocessors (SASS/SCSS basics)

Frontend Frameworks
• React.js basics: components, props, state, hooks
• React Router
• Component lifecycle

Backend Basics
• Node.js and Express.js fundamentals
• REST API creation
• CRUD operations with databases (MongoDB/MySQL)

Databases
• SQL vs NoSQL basics
• Connecting database with backend
• Basic queries, joins, and aggregation

Version Control  Deployment
• GitHub, GitLab basics
• Hosting websites (Netlify, Vercel)
• Environment variables

Other Concepts
• JSON, XML
• Authentication  authorization basics (JWT, OAuth)
• Web security basics (CORS, XSS, SQL Injection)

✅ Web Development Free Courses

❯ HTML & CSS
freecodecamp.org/learn/responsive-web-design/

❯ JavaScript
cs50.harvard.edu/web/

https://t.me/javascript_courses

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

❯ React.js
kaggle.com/learn/intro-to-programming

❯ Node.js & Express
freecodecamp.org/learn/back-end-development-and-apis/

❯ Git & GitHub
lab.github.com/githubtraining

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

❯ Full-Stack Web Dev
fullstackopen.com/en/

https://t.me/webdevcoursefree

https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z

❯ Web Design Basics
openclassrooms.com/en/courses/5265446-build-your-first-web-pages

https://whatsapp.com/channel/0029Vb5dho06LwHmgMLYci1P

❯ API & Microservices
freecodecamp.org/learn/apis-and-microservices/

Double Tap ♥️ For More

SQL Detailed Roadmap
|
| | |-- Fundamentals
| |-- Introduction to Databases
| | |-- What SQL does
| | |-- Relational model
| | |-- Tables, rows, columns
| |-- Keys and Constraints
| | |-- Primary keys
| | |-- Foreign keys
| | |-- Unique and check constraints
| |-- Normalization
| | |-- 1NF, 2NF, 3NF
| | |-- ER diagrams

| | |-- Core SQL
| |-- SQL Basics
| | |-- SELECT, WHERE, ORDER BY
| | |-- GROUP BY and HAVING
| | |-- JOINS: INNER, LEFT, RIGHT, FULL
| |-- Intermediate SQL
| | |-- Subqueries
| | |-- CTEs
| | |-- CASE statements
| | |-- Aggregations
| |-- Advanced SQL
| | |-- Window functions
| | |-- Analytical functions
| | |-- Ranking, moving averages, lag and lead
| | |-- UNION, INTERSECT, EXCEPT

| | |-- Data Management
| |-- Data Types
| | |-- Numeric, text, date, JSON
| |-- Indexes
| | |-- B tree and hash indexes
| | |-- When to create indexes
| |-- Transactions
| | |-- ACID properties
| |-- Views
| | |-- Standard views
| | |-- Materialized views

| | |-- Database Design
| |-- Schema Design
| | |-- Star schema
| | |-- Snowflake schema
| |-- Fact and Dimension Tables
| |-- Constraints for clean data

| | |-- Performance Tuning
| |-- Query Optimization
| | |-- Execution plans
| | |-- Index usage
| | |-- Reducing scans
| |-- Partitioning
| | |-- Horizontal partitioning
| | |-- Sharding basics

| | |-- SQL for Analytics
| |-- KPI calculations
| |-- Cohort analysis
| |-- Funnel analysis
| |-- Churn and retention tables
| |-- Time based aggregations
| |-- Window functions for metrics

| | |-- SQL for Data Engineering
| |-- ETL Workflows
| | |-- Staging tables
| | |-- Transformations
| | |-- Incremental loads
| |-- Data Warehousing
| | |-- Snowflake
| | |-- Redshift
| | |-- BigQuery
| |-- dbt Basics
| | |-- Models
| | |-- Tests
| | |-- Lineage

| | |-- Tools and Platforms
| |-- PostgreSQL
| |-- MySQL
| |-- SQL Server
| |-- Oracle
| |-- SQLite
| |-- Cloud SQL
| |-- BigQuery UI
| |-- Snowflake Worksheets

| | |-- Projects
| |-- Build a sales reporting system
| |-- Create a star schema from raw CSV files
| |-- Design a customer segmentation query
| |-- Build a churn dashboard dataset
| |-- Optimize slow queries in a sample DB
| |-- Create an analytics pipeline with dbt

| | |-- Soft Skills and Career Prep
| |-- SQL interview patterns
| |-- Joins practice
| |-- Window function drills
| |-- Query writing speed
| |-- Git and GitHub
| |-- Data storytelling

| | |-- Bonus Topics
| |-- NoSQL intro
| |-- Working with JSON fields
| |-- Spatial SQL
| |-- Time series tables
| |-- CDC concepts
| |-- Real time analytics

| | |-- Community and Growth
| |-- LeetCode SQL
| |-- Kaggle datasets with SQL
| |-- GitHub projects
| |-- LinkedIn posts
| |-- Open source contributions

Free Resources to learn SQL

• W3Schools SQL
https://www.w3schools.com/sql/

• SQL Programming
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

• SQL Notes
https://whatsapp.com/channel/0029Vb6hJmM9hXFCWNtQX944

• Mode Analytics SQL tutorials
https://mode.com/sql-tutorial/

• Data Analytics Resources
https://t.me/sqlspecialist

• HackerRank SQL practice
https://www.hackerrank.com/domains/sql

• LeetCode SQL problems
https://leetcode.com/problemset/database/

• Data Engineering Resources
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

• Khan Academy SQL basics
https://www.khanacademy.org/computing/computer-programming/sql

• PostgreSQL official docs
https://www.postgresql.org/docs/

• MySQL official docs
https://dev.mysql.com/doc/

• NoSQL Resources
https://whatsapp.com/channel/0029VaxA2hTHgZWe5FpFjm3p

Double Tap ❤️ For More

Artificial Intelligence Roadmap
|
|-- Core Foundations
|   |-- Mathematics
|   |   |-- Linear Algebra
|   |   |-- Calculus
|   |   |-- Probability
|   |   |-- Statistics
|   |
|   |-- Programming
|   |   |-- Python
|   |   |   |-- NumPy
|   |   |   |-- Pandas
|   |   |   |-- Matplotlib
|   |   |-- R
|   |   |-- SQL
|
|-- Classical AI
|   |-- Search Algorithms
|   |   |-- BFS
|   |   |-- DFS
|   |   |-- A*
|   |
|   |-- Optimization
|   |   |-- Gradient Descent
|   |   |-- Convex Optimization
|
|-- Machine Learning
|   |-- Supervised Learning
|   |   |-- Linear Regression
|   |   |-- Logistic Regression
|   |   |-- Decision Trees
|   |   |-- SVM
|   |
|   |-- Unsupervised Learning
|   |   |-- K Means
|   |   |-- Hierarchical Clustering
|   |   |-- PCA
|
|-- Neural Networks
|   |-- Feedforward Networks
|   |-- Backpropagation
|   |-- Activation Functions
|   |-- Loss Functions
|
|-- Deep Learning
|   |-- CNN
|   |-- RNN
|   |-- LSTM
|   |-- GRU
|   |-- Transformers
|   |-- Attention Mechanisms
|
|-- Natural Language Processing
|   |-- Text Preprocessing
|   |-- Embeddings
|   |-- Sequence Models
|   |-- Large Language Models
|   |-- Prompting Techniques
|
|-- Computer Vision
|   |-- Image Processing
|   |-- Object Detection
|   |-- Segmentation
|   |-- Vision Transformers
|
|-- Reinforcement Learning
|   |-- Markov Decision Processes
|   |-- Q Learning
|   |-- Deep Q Networks
|   |-- Policy Gradient Methods
|
|-- AI Tools and Frameworks
|   |-- TensorFlow
|   |-- PyTorch
|   |-- Keras
|   |-- Scikit Learn
|
|-- AI Engineering
|   |-- Model Serving
|   |-- Optimization
|   |-- Quantization
|   |-- ONNX
|
|-- MLOps
|   |-- Model Lifecycle
|   |-- Versioning
|   |-- Monitoring
|   |-- Pipelines
|
|-- Robotics Basics
|   |-- Motion Planning
|   |-- Control Systems
|
|-- Ethics
|   |-- Fairness
|   |-- Bias
|   |-- Privacy
|   |-- Responsible AI

Free Resources to learn Artificial Intelligence 👇👇

Python
• https://t.me/pythondevelopersindia
• https://realpython.com
• https://numpy.org/doc
• https://whatsapp.com/channel/0029VbC0Xa411ulRe5pNJK3E

Math for AI
• https://www.khanacademy.org/math
• https://www.3blue1brown.com
• https://statquest.org

Machine Learning
• https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O
• https://scikit-learn.org/stable/tutorial
• https://t.me/datalemur
• https://course.fast.ai
• https://www.freecodecamp.org/learn/machine-learning-with-python

Deep Learning
• https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t
• https://www.deeplearning.ai
• https://pytorch.org/tutorials
• https://www.tensorflow.org/tutorials

NLP
• https://huggingface.co/learn/nlp-course
• https://developers.google.com/machine-learning/guides/text-classification

Computer Vision
• https://www.pyimagesearch.com
• https://opencv.org

Reinforcement Learning
• https://spinningup.openai.com
• https://gymnasium.farama.org

AI Ethics
• https://ai.google/responsibility
• https://www.microsoft.com/ai/responsible-ai

Like for more ❤️

ENJOY LEARNING 👍👍

Data Science Roadmap
|
|-- Core Foundations
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus Basics
| | |-- Probability
| | |-- Statistics
| |
| |-- Programming
| | |-- Python
| | | |-- NumPy
| | | |-- Pandas
| | | |-- Matplotlib
| | | |-- Seaborn
| | |-- R
| | |-- SQL
|
|-- Data Handling
| |-- Data Collection
| | |-- APIs
| | |-- Web Scraping
| | |-- Database Queries
| |
| |-- Data Cleaning
| | |-- Missing Values
| | |-- Outliers
| | |-- Feature Scaling
| | |-- Encoding
|
|-- Exploratory Data Analysis
| |-- Summary Statistics
| |-- Univariate Analysis
| |-- Bivariate Analysis
| |-- Visualizations
| |-- Correlation Checks
|
|-- Machine Learning
| |-- Supervised Learning
| | |-- Regression
| | |-- Classification
| |
| |-- Unsupervised Learning
| | |-- Clustering
| | |-- PCA
| |
| |-- Model Selection
| | |-- Train Test Split
| | |-- Cross Validation
| | |-- Hyperparameter Tuning
|
|-- Advanced Machine Learning
| |-- Ensemble Methods
| | |-- Random Forest
| | |-- XGBoost
| | |-- LightGBM
| |
| |-- Time Series
| | |-- ARIMA
| | |-- LSTM
| |
| |-- NLP
| | |-- Text Preprocessing
| | |-- TF IDF
| | |-- Word Embeddings
| |
| |-- Deep Learning
| | |-- Neural Networks
| | |-- CNN
| | |-- RNN
| | |-- Transformers
|
|-- Big Data
| |-- PySpark
| |-- Hadoop
| |-- Distributed Processing
|
|-- Model Deployment
| |-- Flask
| |-- FastAPI
| |-- Streamlit
| |-- Docker
| |-- Cloud Deployment
|
|-- MLOps
| |-- Experiment Tracking
| |-- Model Monitoring
| |-- CI CD
|
|-- Domain Knowledge
| |-- Finance
| |-- Healthcare
| |-- Retail
| |-- Marketing
|
|-- Ethics
| |-- Bias
| |-- Interpretability
| |-- Fairness

Free Resources to learn Data Science 👇👇

Python
• https://t.me/pythonproz
• https://www.learnpython.org/
• https://pythonprogramming.net
• https://pandas.pydata.org/docs/

Statistics
• https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
• https://www.khanacademy.org/math/statistics-probability
• https://statquest.org

Machine Learning
• https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O
• https://t.me/datasciencefree
• https://scikit-learn.org/stable/tutorial
• https://www.freecodecamp.org/learn/machine-learning-with-python
• https://course.fast.ai

Deep Learning
• https://www.deeplearning.ai
• https://playground.tensorflow.org

Data Visualization
• https://matplotlib.org/stable/tutorials
• https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
• https://seaborn.pydata.org/tutorial.html

SQL
• https://mode.com/sql-tutorial/introduction-to-sql
• https://t.me/mysqldata

Big Data
• https://spark.apache.org/docs/latest
• https://hadoop.apache.org

Deployment
• https://docs.streamlit.io
• https://fastapi.tiangolo.com

Like for more ❤️

ENJOY LEARNING 👍👍

Web Development Roadmap
|
|-- Core Basics
| |-- How the Web Works
| | |-- Client Server
| | |-- HTTP
| | |-- DNS
| |
| |-- Internet Basics
| | |-- Browsers
| | |-- Developer Tools
| | |-- Debugging
|
|-- Frontend
| |-- HTML
| | |-- Tags
| | |-- Forms
| | |-- Semantics
| |
| |-- CSS
| | |-- Selectors
| | |-- Flexbox
| | |-- Grid
| | |-- Responsive Design
| |
| |-- JavaScript
| | |-- Variables
| | |-- Arrays
| | |-- Objects
| | |-- DOM
| | |-- Fetch API
| | |-- ES6
| |
| |-- Frontend Frameworks
| | |-- React
| | |-- Vue
| | |-- Angular
| |
| |-- UI Libraries
| | |-- Tailwind
| | |-- Bootstrap
| |
| |-- State Management
| | |-- Redux
| | |-- Zustand
| | |-- Vuex
|
|-- Backend
| |-- Programming
| | |-- Node.js
| | |-- Python Django
| | |-- Java Spring Boot
| | |-- PHP Laravel
| |
| |-- Databases
| | |-- SQL
| | |-- PostgreSQL
| | |-- MySQL
| | |-- MongoDB
| |
| |-- APIs
| | |-- REST
| | |-- GraphQL
| | |-- Authentication
|
|-- DevOps Basics
| |-- Git
| |-- GitHub
| |-- CI CD
| |-- Docker
| |-- Linux Basics
|
|-- Testing
| |-- Unit Testing
| |-- Integration Testing
| |-- Jest
| |-- Cypress
|
|-- Deployment
| |-- Netlify
| |-- Vercel
| |-- AWS
| |-- Render
|
|-- Extra Skills
| |-- Web Security
| | |-- OWASP
| | |-- XSS
| | |-- CSRF
| |
| |-- Performance Optimization
| |-- Accessibility
| |-- SEO Basics


Free Resources to learn Web Development 👇👇

HTML CSS JavaScript
• https://www.freecodecamp.org/learn/javascript-v9/
• https://whatsapp.com/channel/0029Vaxox5i5fM5givkwsH0A
• https://developer.mozilla.org/en-US/docs/Web
• https://www.w3schools.com/
• https://cssbattle.dev/
• https://javascript.info/
• https://whatsapp.com/channel/0029VaxfCpv2v1IqQjv6Ke0r

Frontend Projects
• https://frontendmentor.io
• https://whatsapp.com/channel/0029Vax4TBY9Bb62pAS3mX32
• https://codepen.io
• https://build-your-own.org

React
• https://react.dev/learn
• https://scrimba.com/learn/learnreact

Node.js Backend
• https://nodejs.dev
• https://www.theodinproject.com/paths/full-stack-javascript

Django
• https://djangoproject.com
• https://learndjango.com

Git and GitHub
• https://learngitbranching.js.org/
• https://docs.github.com/en
• https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43

DevOps
• https://roadmap.sh/devops
• https://whatsapp.com/channel/0029Vb6btvg4inonBVckgD1U
• https://docker-curriculum.com

SQL
• https://mode.com/sql-tutorial/introduction-to-sql
• https://t.me/mysqldata
• https://whatsapp.com/channel/0029Vb02HXwJf05dAWeMxr0u
• https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

Deployment
• https://vercel.com/docs
• https://docs.netlify.com

Like for more ❤️

ENJOY LEARNING 👍👍

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://t.me/sqlspecialist/738

2. Python

https://www.learnpython.org/

https://t.me/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://t.me/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://t.me/excel_data

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

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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

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 👍👍

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://t.me/datasciencefree

Python Project Ideas: https://t.me/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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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

Free Resources to learn Data Science

Python Free Course

Machine Learning Crash Course

Data Science Course

Google Cloud Generative AI Path

Machine Learning with Python Free Course

Data Science Free Resources

Deep Learning Nanodegree Program with Real-world Projects

AI, Machine Learning and Deep Learning

Python Free Resources

Data Science Interview Process

Useful WhatsApp Channels to learn Data Science & Artificial Intelligence:

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

Artificial intelligence: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y

Data Science Projects: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z

AI Agents: https://whatsapp.com/channel/0029Vb5vWhu0AgW92o23LY0I

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

Deeplearning AI: https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t

Machine Learning: https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O

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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.

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! 👩‍💻👨‍💻

Join @free4unow_backup for more free resources.

Like this post if it helps 😄❤️

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

Please give us credits while sharing: -> https://t.me/free4unow_backup

ENJOY LEARNING 👍👍