bgremove.zip
41.2 MB
the limit is 50 hits only if didnt work please move to src > components > code.jsx and update the Api_key by commented instruction
coding.minku
bgremove.zip
upadate this Api key if didnt work beacuse limit of api can be reach so create a new api key from : https://www.remove.bg/dashboard#api-key and update it
Top 5 GitHub Repositories Every Programmer Should Know! 👇 FreeCodeCamp: https://github.com/freeCodeCamp/freeCodeCamp
30 Seconds of Code: https://github.com/30-seconds/30-seconds-of-code
The Algorithms: https://github.com/TheAlgorithms
Awesome: https://github.com/sindresorhus/awesome
Project-Based Learning: https://github.com/tuvtran/project-based-learning
30 Seconds of Code: https://github.com/30-seconds/30-seconds-of-code
The Algorithms: https://github.com/TheAlgorithms
Awesome: https://github.com/sindresorhus/awesome
Project-Based Learning: https://github.com/tuvtran/project-based-learning
JavaScript-Handwritten-Notes.pdf
49.8 MB
Javascript Handwritten Notes In English
Telegram to be Banned in my region (India ) So join WhatsApp Chanel Now : 👇
https://whatsapp.com/channel/0029Va9UqI99hXF6JIFYPx2x
https://whatsapp.com/channel/0029Va9UqI99hXF6JIFYPx2x
WhatsApp.com
coding minku | WhatsApp Channel
coding minku WhatsApp Channel. Hello 👋 , here you will get source code of web and app projects and components like HTML || CSS || JAVASCRIPT || JAVA || PYTHON || CPP || PHP And many more .. 168 followers
📅 Month 1: Foundations & Problem-Solving
✅ Python + JavaScript Basics (variables, loops, functions, OOP)
✅ Data Structures & Algorithms (arrays, recursion, linked lists, sorting)
✅ Solve 30-50 coding problems on LeetCode / Codeforces
✅ Build mini projects (calculator, to-do list, number guessing game)
📌 Resources: NeetCode, CS50, MDN Docs
---
📅 Month 2: Frontend & Backend Development
✅ Frontend: Learn React.js + Tailwind CSS (components, hooks, state management)
✅ Backend: Learn Node.js + Express.js + MongoDB (APIs, authentication, CRUD)
✅ Project: Build a full-stack To-Do app with user authentication
📌 Resources: React Docs, The Odin Project
---
📅 Month 3: Real-World Projects + Interview Prep
✅ Build 2-3 Full-Stack Projects (E-commerce, Blog, Social Media Clone)
✅ Solve 50+ LeetCode problems (medium-level DSA)
✅ Learn System Design Basics (scalability, database indexing)
✅ Revise Resume & LinkedIn Profile for job applications
📌 Resources: LeetCode, Roadmap.sh
---
🎯 "Follow this roadmap and land your first developer job in just 3 months!"
✅ Python + JavaScript Basics (variables, loops, functions, OOP)
✅ Data Structures & Algorithms (arrays, recursion, linked lists, sorting)
✅ Solve 30-50 coding problems on LeetCode / Codeforces
✅ Build mini projects (calculator, to-do list, number guessing game)
📌 Resources: NeetCode, CS50, MDN Docs
---
📅 Month 2: Frontend & Backend Development
✅ Frontend: Learn React.js + Tailwind CSS (components, hooks, state management)
✅ Backend: Learn Node.js + Express.js + MongoDB (APIs, authentication, CRUD)
✅ Project: Build a full-stack To-Do app with user authentication
📌 Resources: React Docs, The Odin Project
---
📅 Month 3: Real-World Projects + Interview Prep
✅ Build 2-3 Full-Stack Projects (E-commerce, Blog, Social Media Clone)
✅ Solve 50+ LeetCode problems (medium-level DSA)
✅ Learn System Design Basics (scalability, database indexing)
✅ Revise Resume & LinkedIn Profile for job applications
📌 Resources: LeetCode, Roadmap.sh
---
🎯 "Follow this roadmap and land your first developer job in just 3 months!"
Want To Learn AI , ML DATA SCIENCE in 2025 Then Follow This Roadmap 👇 Step 1: Master Python & SQL 🐍🗄️
✅ Python Basics: Loops, Functions, OOP, Exception Handling.
✅ Data Science Libraries: NumPy, Pandas, Matplotlib, Seaborn.
✅ SQL Mastery: Joins, Indexing, Window Functions, Query Optimization.
🔗 Resources:
Python for Everybody (Free)
SQL for Data Science (Free)
---
Step 2: Build Strong Math & Stats Foundations 📊🧮
✅ Linear Algebra: Vectors, Matrices, Eigenvalues.
✅ Probability & Statistics: Bayes Theorem, Normal Distribution, Hypothesis Testing.
✅ Calculus for ML: Differentiation, Gradients, Partial Derivatives.
🔗 Resources:
Khan Academy - Linear Algebra
StatQuest YouTube Channel
---
Step 3: Learn Machine Learning Algorithms 🤖📈
✅ Supervised Learning: Regression, Decision Trees, SVM, Naive Bayes.
✅ Unsupervised Learning: Clustering (K-Means, DBSCAN), PCA, Anomaly Detection.
✅ Model Evaluation: Bias-Variance Tradeoff, Overfitting, Cross-Validation.
🔗 Resources:
Andrew Ng's ML Course (Free)
Hands-On ML with Scikit-Learn, Keras, and TensorFlow (Book)
---
Step 4: Explore Deep Learning & NLP 🧠💡
✅ Neural Networks Basics: Forward/Backward Propagation, Activation Functions.
✅ Computer Vision: CNNs, Object Detection (YOLO, Faster R-CNN).
✅ NLP: Transformers, BERT, LLMs, Hugging Face.
🔗 Resources:
Deep Learning Specialization - Andrew Ng
Hugging Face Transformers Course (Free)
---
Step 5: Work on Real-World Projects 🔥💻
✅ End-to-End ML Pipelines: Data Cleaning, Feature Engineering, Model Deployment.
✅ Build AI Applications: Chatbots, Image Classification, AI Content Generators.
✅ Deploy Models: Flask, FastAPI, Streamlit, Docker.
🔗 Resources:
Kaggle Competitions & Datasets
Full-Stack Deep Learning Course
---
Step 6: Stay Updated & Network 🌍📚
✅ Follow AI Experts: Yann LeCun, Andrew Ng, Geoffrey Hinton.
✅ Read AI Blogs & Research: Papers with Code, arXiv, Towards Data Science.
✅ Join Communities: Kaggle, LinkedIn, Discord, X (Twitter).
🔗 Resources:
Towards Data Science Blog
Papers with Code
✅ Python Basics: Loops, Functions, OOP, Exception Handling.
✅ Data Science Libraries: NumPy, Pandas, Matplotlib, Seaborn.
✅ SQL Mastery: Joins, Indexing, Window Functions, Query Optimization.
🔗 Resources:
Python for Everybody (Free)
SQL for Data Science (Free)
---
Step 2: Build Strong Math & Stats Foundations 📊🧮
✅ Linear Algebra: Vectors, Matrices, Eigenvalues.
✅ Probability & Statistics: Bayes Theorem, Normal Distribution, Hypothesis Testing.
✅ Calculus for ML: Differentiation, Gradients, Partial Derivatives.
🔗 Resources:
Khan Academy - Linear Algebra
StatQuest YouTube Channel
---
Step 3: Learn Machine Learning Algorithms 🤖📈
✅ Supervised Learning: Regression, Decision Trees, SVM, Naive Bayes.
✅ Unsupervised Learning: Clustering (K-Means, DBSCAN), PCA, Anomaly Detection.
✅ Model Evaluation: Bias-Variance Tradeoff, Overfitting, Cross-Validation.
🔗 Resources:
Andrew Ng's ML Course (Free)
Hands-On ML with Scikit-Learn, Keras, and TensorFlow (Book)
---
Step 4: Explore Deep Learning & NLP 🧠💡
✅ Neural Networks Basics: Forward/Backward Propagation, Activation Functions.
✅ Computer Vision: CNNs, Object Detection (YOLO, Faster R-CNN).
✅ NLP: Transformers, BERT, LLMs, Hugging Face.
🔗 Resources:
Deep Learning Specialization - Andrew Ng
Hugging Face Transformers Course (Free)
---
Step 5: Work on Real-World Projects 🔥💻
✅ End-to-End ML Pipelines: Data Cleaning, Feature Engineering, Model Deployment.
✅ Build AI Applications: Chatbots, Image Classification, AI Content Generators.
✅ Deploy Models: Flask, FastAPI, Streamlit, Docker.
🔗 Resources:
Kaggle Competitions & Datasets
Full-Stack Deep Learning Course
---
Step 6: Stay Updated & Network 🌍📚
✅ Follow AI Experts: Yann LeCun, Andrew Ng, Geoffrey Hinton.
✅ Read AI Blogs & Research: Papers with Code, arXiv, Towards Data Science.
✅ Join Communities: Kaggle, LinkedIn, Discord, X (Twitter).
🔗 Resources:
Towards Data Science Blog
Papers with Code
Which type of reel / Video u want to see on my insta page drop in discussion 👇