🚀 How to Become a Self-Taught AI Developer?
AI is transforming the world, and the best part? You don’t need a formal degree to break into the field! With the right roadmap and hands-on practice, anyone can become an AI developer. Here’s how you can do it:
1️⃣ Master the Fundamentals of Programming
Start with Python, as it’s the most popular language for AI. Learn data structures, algorithms, and object-oriented programming (OOP). Practice coding on LeetCode and HackerRank.
👉How to get started Python: https://www.youtube.com/watch?v=EGdhnSEWKok
How to Create & Use Python Virtual Environments | ML Project Setup + GitHub Actions CI/CD https://youtu.be/qYYYgS-ou7Q
👉Beginner's Guide to Python Programming. Getting started now: https://youtu.be/ISv6XIl1hn0
👉Data Structures with Projects full tutorial for beginners
https://www.youtube.com/watch?v=lbdKQI8Jsok
👉OOP in Python - beginners Crash Course https://www.youtube.com/watch?v=I7z6i1QTdsw
2️⃣ Build a Strong Math Foundation
AI relies on:
🔹 Linear Algebra – Matrices, vectors (used in deep learning) https://youtu.be/BNa2s6OtWls
🔹 Probability & Statistics – Bayesian reasoning, distributions https://youtube.com/playlist?list=PL0nX4ZoMtjYEl_1ONxAZHu65DPCQcsHmI&si=tAz0B3yoATAjE8Fx
🔹 Calculus – Derivatives, gradients (used in optimization)
📚 Learn from 3Blue1Brown, Khan Academy, or MIT OpenCourseWare.
3️⃣ Learn Machine Learning (ML)
Start with traditional ML before deep learning:
✔ Supervised Learning – Linear regression, decision trees https://youtube.com/playlist?list=PL0nX4ZoMtjYGV8Ff_s2FtADIPfwlHst8B&si=buC-eP3AZkIjzI_N
✔ Unsupervised Learning – Clustering, PCA
✔ Reinforcement Learning – Q-learning, deep Q-networks
🔗 Best course? Andrew Ng’s ML Course on Coursera.
4️⃣ Dive into Deep Learning
Once comfortable with ML, explore:
⚡ Neural Networks (ANNs, CNNs, RNNs, Transformers)
⚡ TensorFlow & PyTorch (Industry-standard deep learning frameworks)
⚡ Computer Vision & NLP
Try Fast.ai or the Deep Learning Specialization by Andrew Ng.
5️⃣ Build Real-World Projects
The best way to learn AI? DO AI. 🚀
💡 Train models with Kaggle datasets
💡 Build a chatbot, image classifier, or recommendation system
💡 Contribute to open-source AI projects
6️⃣ Stay Updated & Join the AI Community
AI evolves fast! Stay ahead by:
🔹 Following Google AI, OpenAI, DeepMind
🔹 Engaging in Reddit r/MachineLearning, LinkedIn AI discussions
🔹 Attending AI conferences like NeurIPS & ICML
7️⃣ Create a Portfolio & Apply for AI Roles
📌 Publish projects on GitHub
📌 Share insights on Medium/Towards Data Science
📌 Network on LinkedIn & Kaggle
No CS degree? No problem! AI is about curiosity, consistency, and hands-on experience. Start now, keep learning, and let’s build the future with AI. 🚀
Tagging AI learners & enthusiasts: What’s your AI learning journey like? Let’s connect!. 🔥👇
#AI #MachineLearning #DeepLearning #Python #ArtificialIntelligence #SelfTaught
AI is transforming the world, and the best part? You don’t need a formal degree to break into the field! With the right roadmap and hands-on practice, anyone can become an AI developer. Here’s how you can do it:
1️⃣ Master the Fundamentals of Programming
Start with Python, as it’s the most popular language for AI. Learn data structures, algorithms, and object-oriented programming (OOP). Practice coding on LeetCode and HackerRank.
👉How to get started Python: https://www.youtube.com/watch?v=EGdhnSEWKok
How to Create & Use Python Virtual Environments | ML Project Setup + GitHub Actions CI/CD https://youtu.be/qYYYgS-ou7Q
👉Beginner's Guide to Python Programming. Getting started now: https://youtu.be/ISv6XIl1hn0
👉Data Structures with Projects full tutorial for beginners
https://www.youtube.com/watch?v=lbdKQI8Jsok
👉OOP in Python - beginners Crash Course https://www.youtube.com/watch?v=I7z6i1QTdsw
2️⃣ Build a Strong Math Foundation
AI relies on:
🔹 Linear Algebra – Matrices, vectors (used in deep learning) https://youtu.be/BNa2s6OtWls
🔹 Probability & Statistics – Bayesian reasoning, distributions https://youtube.com/playlist?list=PL0nX4ZoMtjYEl_1ONxAZHu65DPCQcsHmI&si=tAz0B3yoATAjE8Fx
🔹 Calculus – Derivatives, gradients (used in optimization)
📚 Learn from 3Blue1Brown, Khan Academy, or MIT OpenCourseWare.
3️⃣ Learn Machine Learning (ML)
Start with traditional ML before deep learning:
✔ Supervised Learning – Linear regression, decision trees https://youtube.com/playlist?list=PL0nX4ZoMtjYGV8Ff_s2FtADIPfwlHst8B&si=buC-eP3AZkIjzI_N
✔ Unsupervised Learning – Clustering, PCA
✔ Reinforcement Learning – Q-learning, deep Q-networks
🔗 Best course? Andrew Ng’s ML Course on Coursera.
4️⃣ Dive into Deep Learning
Once comfortable with ML, explore:
⚡ Neural Networks (ANNs, CNNs, RNNs, Transformers)
⚡ TensorFlow & PyTorch (Industry-standard deep learning frameworks)
⚡ Computer Vision & NLP
Try Fast.ai or the Deep Learning Specialization by Andrew Ng.
5️⃣ Build Real-World Projects
The best way to learn AI? DO AI. 🚀
💡 Train models with Kaggle datasets
💡 Build a chatbot, image classifier, or recommendation system
💡 Contribute to open-source AI projects
6️⃣ Stay Updated & Join the AI Community
AI evolves fast! Stay ahead by:
🔹 Following Google AI, OpenAI, DeepMind
🔹 Engaging in Reddit r/MachineLearning, LinkedIn AI discussions
🔹 Attending AI conferences like NeurIPS & ICML
7️⃣ Create a Portfolio & Apply for AI Roles
📌 Publish projects on GitHub
📌 Share insights on Medium/Towards Data Science
📌 Network on LinkedIn & Kaggle
No CS degree? No problem! AI is about curiosity, consistency, and hands-on experience. Start now, keep learning, and let’s build the future with AI. 🚀
Tagging AI learners & enthusiasts: What’s your AI learning journey like? Let’s connect!. 🔥👇
#AI #MachineLearning #DeepLearning #Python #ArtificialIntelligence #SelfTaught