Introduction to Computer Science and Programming in Python
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⌛️Labs
🧭 Problem Sets / Codes
Created by 👨🏫: MIT
Video lessons 🎥
Slides and code 👨💻
🔗 COURSE LINK
📣No registration or download required
🆓 Free Online Course
🏃♂️ Self paced
Resources 💻 : Slides & Notes
⌛️Labs
🧭 Problem Sets / Codes
Created by 👨🏫: MIT
Video lessons 🎥
Slides and code 👨💻
🔗 COURSE LINK
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Artificial Intelligence isn't easy!
It’s the cutting-edge field that enables machines to think, learn, and act like humans.
To truly master Artificial Intelligence, focus on these key areas:
0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.
1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.
2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.
3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.
4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).
5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.
6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.
7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications.
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It’s the cutting-edge field that enables machines to think, learn, and act like humans.
To truly master Artificial Intelligence, focus on these key areas:
0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees.
1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques.
2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models.
3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots.
4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics).
5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models.
6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias.
7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications.
8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world.
9. Staying Updated with AI Research: AI is an ever-evolving field—stay on top of cutting-edge advancements, papers, and new algorithms.
Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity.
💡 Embrace the journey of learning and building systems that can reason, understand, and adapt.
⏳ With dedication, hands-on practice, and continuous learning, you’ll contribute to shaping the future of intelligent systems!
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
#ai #datascience
👍2
Natural Language Processing Projects.pdf
13.2 MB
Natural Language Processing Projects
Akshay Kulkarni, 2022
Akshay Kulkarni, 2022
Python Machine Learning Projects.pdf
871.9 KB
Python Machine Learning Projects
DigitalOcean, 2022
DigitalOcean, 2022
R Projects For Dummies.pdf
5.6 MB
R Projects for Dummies
Joseph Schmuller, 2018
Joseph Schmuller, 2018
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A brief introduction to object oriented programming OOP in JavaScript programming language in a practical way with simple examples
👍2
--- Git Commands ---
🏗️ git init | Initialize a new Git repository
🔄 git clone <repo> | Clone a repository
📊 git status | Check the status of your repository
➕ git add <file> | Add a file to the staging area
📝 git commit -m "message" | Commit changes with a message
🚀 git push | Push changes to a remote repository
⬇️ git pull | Fetch and merge changes from a remote repository
--- Branching ---
🌿 git branch | List branches
🌱 git branch <name> | Create a new branch
🔀 git checkout <branch> | Switch to a branch
🔧 git merge <branch> | Merge a branch into the current branch
🔄 git rebase <branch> | Reapply commits on top of another base branch
--- Undo & Fix Mistakes ---
🔙 git reset --soft HEAD~1 | Undo last commit but keep changes
🚫 git reset --hard HEAD-1 | Undo last commit and discard changes
⏪ git revert <commit> | Create a new commit that undoes changes from a specific commit
--- Logs & History ---
📜 git log | Show commit history
🌐 git log --oneline --graph --all | Pretty graph of commit history
--- Stashing ---
🎒 git stash | Save changes without committing
🎭 git stash pop | Apply stashed changes and remove them from stash
--- Remote & Collaboration ---
🌍 git remote -v | View remote repositories
📡 git fetch | Fetch changes without merging
🕵️ git diff | Compare changes
🏗️ git init | Initialize a new Git repository
🔄 git clone <repo> | Clone a repository
📊 git status | Check the status of your repository
➕ git add <file> | Add a file to the staging area
📝 git commit -m "message" | Commit changes with a message
🚀 git push | Push changes to a remote repository
⬇️ git pull | Fetch and merge changes from a remote repository
--- Branching ---
🌿 git branch | List branches
🌱 git branch <name> | Create a new branch
🔀 git checkout <branch> | Switch to a branch
🔧 git merge <branch> | Merge a branch into the current branch
🔄 git rebase <branch> | Reapply commits on top of another base branch
--- Undo & Fix Mistakes ---
🔙 git reset --soft HEAD~1 | Undo last commit but keep changes
🚫 git reset --hard HEAD-1 | Undo last commit and discard changes
⏪ git revert <commit> | Create a new commit that undoes changes from a specific commit
--- Logs & History ---
📜 git log | Show commit history
🌐 git log --oneline --graph --all | Pretty graph of commit history
--- Stashing ---
🎒 git stash | Save changes without committing
🎭 git stash pop | Apply stashed changes and remove them from stash
--- Remote & Collaboration ---
🌍 git remote -v | View remote repositories
📡 git fetch | Fetch changes without merging
🕵️ git diff | Compare changes
👍4
🚀 5 Steps to learn Full Stack Web Development:
1. 📚 Begin with Basics:
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- Dive into front-end magic with a JavaScript framework like React ⚛️, Angular 🅰️, or Vue.js 🖖. Craft beautiful user interfaces.
3. 🌐 Back-End Development:
- Venture into the server-side realm by picking a language (e.g., Node.js 🚀, Python 🐍, Ruby 💎) and its associated framework.
4. 🗄️ Databases and Data Management:
- Explore the world of databases (MySQL 🐬, PostgreSQL 🐘, MongoDB 🍃) to store and retrieve data efficiently.
5. 🌐 Full-Stack Integration:
- Unite your powers! Combine front-end and back-end skills to create full-fledged web applications 🚀.
1. 📚 Begin with Basics:
- Start with the essentials: Learn HTML 🌐, CSS 🎨, and JavaScript 🧪 as the building blocks.
2. 🖼️ Front-End Development:
- Dive into front-end magic with a JavaScript framework like React ⚛️, Angular 🅰️, or Vue.js 🖖. Craft beautiful user interfaces.
3. 🌐 Back-End Development:
- Venture into the server-side realm by picking a language (e.g., Node.js 🚀, Python 🐍, Ruby 💎) and its associated framework.
4. 🗄️ Databases and Data Management:
- Explore the world of databases (MySQL 🐬, PostgreSQL 🐘, MongoDB 🍃) to store and retrieve data efficiently.
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