This roadmap combines insights from top-tier university curricula, alongside relevant free online resources. Follow these steps to build a strong foundation and advance in the field of AI engineering.
| Courses | Link | Status |
|---|---|---|
| Calculus 1 | Professor Leonard: Calculus 1 Playlist | In Progress |
| Calculus 2 | Professor Leonard: Calculus 1 Playlist | Incompleted |
| Calculus 3 | Professor Leonard: Calculus 3 Playlist | Incompleted |
| Linear Algebra | MIT OCW: 18.06SC Linear Algebra | Incompleted |
| Probability and Statistics | Professor Leonard: Statistics | Incompleted |
| Python | Free Code Camp: Learn Python - Full Course for Beginners | Incompleted |
| Data Structures and Algorithms | MIT OCW: 6.006 Introduction to Algorithms | In Progress |
P.s.: Instead of Calculus and Linear Algebra, consider Mathematics for Machine Learning. This course covers essential math concepts from Calculus and Linear Algebra, focusing on their direct application to AI engineering.
| Courses | Link | Status |
|---|---|---|
| Supervised, Unsupervised Learning, and Model Evaluation | Standford Online: CS229 Machine Learning | Incompleted |
| Feature Engineering and Selection | Kaggle: Feature Engineering Tutorial | Incompleted |
| Courses | Link | Status |
|---|---|---|
| Neural Networks, Backpropagation, Optimization | MIT Deep Learning: 6.S191 Introduction to Deep Learning | Incompleted |
| Convolutional Neural Networks (CNNs) | Stanford Online: CS231n Convolutional Neural Networks for Visual Recognition | Incompleted |
| Recurrent Neural Networks (RNNs) and Transformers | Stanford Online: CS224n Natural Language Processing with Deep Learning | Incompleted |
| Generative Adversarial Networks (GANs) | Coursera: Generative Adversarial Networks (GANs) Specialization | Incompleted |
| Reinforcement Learning (RL) | Stanford Online: CS234 Reinforcement Learning | Incompleted |
| Graph Neural Networks (GNNs) | Stanford CS224W: Machine Learning with Graphs | Incompleted |
| Transfer Learning and Fine-Tuning | Hugging Face Course | Incompleted |
| Deep Reinforcement Learning | Spinning Up in Deep RL | Incompleted |
- Natural Language Processing (NLP)
- Focus on NLP techniques and models.
- Computer Vision
- Explore advanced computer vision algorithms and architectures.
- Reinforcement Learning (RL)
- Focus on solving complex decision-making problems.
- Robotics
- Integrate AI with physical systems for intelligent automation.
- Generative AI
- Create novel content and solve creative problems using AI.
- Kaggle
- Participate in competitions to apply your skills to real-world problems.
- GitHub
- Contribute to open-source AI projects.
- Personal Projects
- Build your own AI applications to solve interesting challenges.
- Stay Informed
- Follow AI conferences, read blogs, and join online communities.
- Network
- Connect with other AI enthusiasts and professionals.
- Soft Skills
- Hone your communication, collaboration, and problem-solving skills.
This comprehensive roadmap is designed to equip you with the knowledge and skills needed to excel as an AI engineer. Remember, this is a journey, and continuous learning is key to staying ahead in this rapidly evolving field. Embrace the challenge and enjoy the process!