full YouTube-based roadmap from zero to AI engineer
Perfect! Since you want a full YouTube-based roadmap from zero to AI engineer, here’s a step-by-step plan with actual channels and playlists that are free and beginner-friendly. You can follow them in order:
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Step 1: Learn Python (Programming Foundation)
Channel: freeCodeCamp.org
Playlist / Video: Python for Beginners - Full Course (4h 20m)
Learn variables, loops, functions, lists, dictionaries, file handling.
Python is the main language for AI.
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Step 2: Math Foundations (Linear Algebra, Probability, Statistics)
Channel: Khan Academy
Playlists / Videos:
Linear Algebra: https://www.youtube.com/playlist?list=PL1328115D3D8A2566
Probability & Statistics: https://www.youtube.com/playlist?list=PL1328115D3D8A2566
Focus on basics; you don’t need to master everything at first.
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Step 3: Introduction to Machine Learning
Channel: freeCodeCamp.org
Video / Playlist: Machine Learning with Python - Full Course (6h 30m)
Covers supervised learning, unsupervised learning, regression, classification.
Hands-on with Python.
Alternative / Extra: Simplilearn - Machine Learning Full Course (also beginner-friendly)
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Step 4: Deep Learning
Channel: freeCodeCamp.org
Video: Deep Learning Full Course - 2023 (10h)
Neural networks, CNNs, RNNs, NLP.
Practical coding with Python & TensorFlow / Keras.
Extra channel for short explanations: DeepLizard
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Step 5: Hands-On Projects & Portfolio
Channels / Resources:
Kaggle Learn — mini-projects and competitions.
Google Colab tutorials: search “Python AI project Colab” on YouTube.
Example projects to do:
Image recognition (MNIST digits, cats vs dogs)
Chatbot using NLP
Predicting stock or sales data
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Step 6: AI Engineer Job Prep
Put projects on GitHub.
Learn how to deploy models (Flask, Streamlit).
Optional: fast.ai for practical AI applications.
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Summary / Order to Follow
1. Python → freeCodeCamp Python course
2. Math → Khan Academy (Linear Algebra + Probability & Stats)
3. Machine Learning → freeCodeCamp ML course
4. Deep Learning → freeCodeCamp Deep Learning course + DeepLizard for concepts
5. Projects → Kaggle + Google Colab
6. Portfolio → GitHub + deploy apps
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💡 Tip: Focus on doing projects while learning. Even small projects make you ready for an entry-level AI role much faster than just watching videos.
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If you want, I can make a compressed 1-year schedule using these YouTube resources that tells
you what to watch and practice each month, so you can reach your first AI engineer role faster.
Do you want me to do that?
