AI and Machine Learning Curriculum

If you follow me on Social Media, then you already know that I recently talked about the top 10 most in-demand tech skills in 2024. and due to so many demands, I have decided to create a curriculum for a comprehensive program on AI and Machine Learning. 

 Below is a general roadmap, curriculum and time table to get you started, along with links to relevant resources. Keep in mind that this is a suggested path, and you may adjust it based on your prior knowledge, preferences, and the specific areas of AI and machine learning that interest you.

1. Foundations:

• Mathematics:
  • Linear Algebra
  • Calculus
  • Probability and Statistics
• Programming:
  • Python
    • Codecademy - Learn Python

2. Introduction to Machine Learning:

• Courses:
  • Coursera - Machine Learning by Andrew Ng
  • edX - Principles of Machine Learning by Microsoft
• Books:
  • "Introduction to Machine Learning with Python" by Andreas C. Müller and Sarah Guido
  • "Pattern Recognition and Machine Learning" by Christopher M. Bishop

3. Deep Learning:

• Courses:
  • Udemy - Deep Learning Specialization (free Udemy course)
  • Fast.ai - Practical Deep Learning for Coders
• Books:
  • "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
  • "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron

4. AI Applications and Specializations:

• Natural Language Processing (NLP):
  • Coursera - Natural Language Processing in TensorFlow
• Computer Vision:
  • Coursera - Convolutional Neural Networks by Andrew Ng
  • Stanford CS231n - Convolutional Neural Networks for Visual Recognition
• Reinforcement Learning:
  • Coursera - Reinforcement Learning Specialization by University of Alberta

5. Advanced Topics:

• Generative Adversarial Networks (GANs):
  • Coursera - Generative Adversarial Networks (GANs) Specialization
• Explainable AI and Fairness:
  • Interpretable Machine Learning
  • Fairness and Abstraction in Sociotechnical Systems

6. Practical Projects:

• Work on real-world projects to apply your knowledge and build your portfolio. Platforms like Kaggle and GitHub can be great resources.

7. Stay Updated:

• Follow conferences (e.g., NeurIPS, ICML), journals, and blogs to stay informed about the latest developments.

Additional Resources:

• GitHub - Awesome Machine Learning
• TensorFlow Tutorials
• PyTorch Tutorials

Remember, learning is a continuous process in the rapidly evolving field of AI and machine learning. Be open to exploring new topics and adapting your learning path based on emerging trends. Good luck!

 1.1 TIME TABLE

Weeks 1-4: Foundations

Mathematics:

• Linear Algebra:
  • W3Schools - Linear Algebra
  • JavaTpoint - Linear Algebra Tutorial
• Calculus:
  • W3Schools - Calculus
  • JavaTpoint - Calculus Tutorial
• Probability and Statistics:
  • W3Schools - Statistics
  • JavaTpoint - Statistics Tutorial

Programming:

• Python:
  • W3Schools - Python Tutorial
  • JavaTpoint - Python Tutorial

Weeks 5-8: Introduction to Machine Learning

Courses:

• Code Academy - Machine Learning
• Microsoft Learn - Principles of Machine Learning

Books:

• "Introduction to Machine Learning with Python" by Andreas C. Müller and Sarah Guido
• "Pattern Recognition and Machine Learning" by Christopher M. Bishop

Weeks 9-12: Deep Learning

Courses:

• Udemy - Deep Learning Specialization (free Udemy course)
• Fast.ai - Practical Deep Learning for Coders

Books:

• "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
• "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron

Weeks 13-16: AI Applications and Specializations

Natural Language Processing (NLP):

• Tensorflow - Natural Language Processing in TensorFlow
• Tutorialspoint - NLP Tutorial

Computer Vision:

• Simplilearn - Convolutional Neural Networks
• JavaTpoint - Computer Vision Tutorial

Reinforcement Learning:

• deeplizard - Reinforcement Learning Specialization
• Learndatasci - Reinforcement Learning

Weeks 17-20: Advanced Topics

Generative Adversarial Networks (GANs):

• Freecodecamp - Generative Adversarial Networks (GANs) Specialization
• JavaTpoint - GANs Tutorial

Explainable AI and Fairness:

• Interpretable Machine Learning
• Fairness and Abstraction in Sociotechnical Systems

Weeks 21-24: Practical Projects

• Work on real-world projects to apply your knowledge. Platforms like Kaggle and GitHub can be great resources.
• Search for open source projects on Github and Linkedin and contribute

Weeks 25-28: Stay Updated

• Follow conferences (e.g., NeurIPS, ICML), journals, and blogs to stay informed about the latest developments.

Additional Resources:

• GitHub - Awesome Machine Learning
• TensorFlow Tutorials
• PyTorch Tutorials

Remember to adjust the pace based on your understanding and availability. Learning is a dynamic process, and practical application is key to mastery. Good luck!

 

 

Practical Deep Learning for Coders - Practical Deep Learning

A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.

course.fast.ai