Six Month Study Plan

It has only been three and half weeks since I decided to study AI and machine learning full-time. So far, so exhilarating. I've learned basic Python with the University of Michigan course series on Coursera, and done some projects with it, two genetic algorithms and also basic data processing to get familiar with using SQL, JSON and interacting with an API (Geoapify).

I've also learned a lot of maths and have a reasonable basic high level understanding of machine learning now thanks to free courses from Harvard and a wonderful YouTube series by Dan Shiffman. I've also found a local monthly meet-up group called Nottingham Data Science and AI, and I've booked myself in to go to all their upcoming talks and social events.

I've concluded that the niche I want to specialise in is where AI meets photography. This is a natural fit for me for two reasons:

1) I was a professional photographer.

2) My Master's degree thesis and subsequent job as a software developer was in the field of machine vision.

Study Plan

Here, then, is my study plan for the next six months, with that niche in mind. I'm broadly focusing on one topic per month, although this may change. I will continue to use Python thoughout all of this and do relevant projects as much as possible. Naturally, this study plan was created with help from ChatGPT4o, which is wonderful and something I use all the time.

  • June - Maths
    • Precalculus.
    • Calculus.
    • Linear Algebra.
    • Probability and Statistics.
  • July - Machine Learning and Deep Learning
    • Understanding of neural networks, particularly Convolutional Neural Networks (CNNs) for image processing.
    • Familiarity with frameworks like TensorFlow, Keras, and PyTorch.
  • August - Image Processing Techniques
    • Knowledge of basic and advanced image processing techniques.
    • Proficiency in OpenCV for handling image data.
  • September - Generative Models
    • Understanding Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
    • Ability to implement and tweak these models for tasks like image synthesis and inpainting.
  • October - Computer Vision
    • Skills in object detection (e.g., YOLO, Faster R-CNN) and image segmentation (e.g., U-Net).
    • Experience with facial recognition and analysis algorithms.
  • November - Data Science and Statistical Analysis
    • Strong foundation in statistics and data analysis.
    • Experience with data preprocessing, augmentation, and handling large datasets.


These are heavyweight topics, none of which can be mastered in a month. But after spending a month on each, getting as far as I can get in the time, I figure I'll be a great position to know what to focus on next.

Thanks for following my journey!