Roadmap to Becoming an AI Engineer

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.

Step 1: Solidify Your Foundation

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.

Step 2: Master Machine Learning

Courses	Link	Status
Supervised, Unsupervised Learning, and Model Evaluation	Standford Online: CS229 Machine Learning	Incompleted
Feature Engineering and Selection	Kaggle: Feature Engineering Tutorial	Incompleted

Step 3: Dive into Deep Learning

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

Step 4: Choose Your Specialization (Optional)

• 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.

Step 5: Gain Hands-On Experience

• 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.

Additional Tips

• 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!