Artificial Intelligence
To make a career in Artificial Intelligence (AI), you need:
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Computer Science Fundamentals:
- Python: Python for Everybody.
- Data Structures & Algorithms: Code With Mosh – The Ultimate Data Structures & Algorithms.
- Git: you can learn git beside the previous subjects, it is not really prerequisite for anything but it will makes your life a lot easier if you know if.
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Mathematics and Statistics:
**TBF IDK which is prerequisite for the other, as such I just used an unordered list here : **. -
Linear Algebra: Udemy Become a Linear Algebra Master.
Try this one or this one if “Udemy Become a Linear Algebra Master” was not helpful at all.
Not sure TBH if this one is one that you should learn as AI engineer, but any how I will keep its link and will remove it later if I realized that it is not needed :smile:.
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Calculus:
If this course did not work check this one.
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Probability and statistics: Udemy – Become a Probability & Statistics Master.
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- Machine Learning (ML):
- Introduction to Machine Learning.
- Advanced Machine Learning: could not find anything as of now, but I guess when you finish the previous step you’ll have a pretty good understanding of which course do you need and how you can find it.
- Deep Learning: There are quite a few courses online and I did not dare to add link to them. You can find them in downloadly.ir for example. But before anything it is highly perceptive of you if you seek a professional’s giudance on this matter.
- Deep Learning.
- Neural Networks.
- Convolutional Neural Networks (CNNs).
- Recurrent Neural Networks (RNNs).
- Artificial Intelligence Fundamentals:
- Introduction to AI
- AI Techniques.
- Knowledge Representation.
- Natural Language Processing (NLP):
- Natural Language Processing.
- Computational Linguistics.
- Text Mining.
- Computer Vision:
- Computer Vision.
- Image Processing.
- Object Recognition.
- Robotics:
- Robotics.
- Autonomous Systems.
- Robot Programming.
- Ethics in AI:
- Ethics in AI.
- AI Governance.
- AI and Society.
- Data Science and Big Data:
- Data Science.
- Big Data Analytics.
- Data Engineering.