this is comprehensive roadmap for machine learning , deep learning and computer vision . By : Dr.Mostafa I. Saad

visit the source : https://youtube.com/playlist?list=PLPt2dINI2MIYdFB4H9bTmen9H6sbSzz2_&si=-Tllise7ll5N2oyH

-- Roadmaps nature

It is not mandatory when you write a roadmap this meaning that i followed this roadmap literally for many reasons like may be there is update in new courses which is more beneficial than the one i have studied and so on .

Try to search about their history

in the online courses are not enough , generally it tells you to complete some functionalities which are very trivial and easy

You should do the whole project by yourself , not just some functionalities

try to search , use GPT , and try to find an abstraction for this topic as / a powerpoint/ from high level universities before getting deeper in that topic

generally after the second year of your college as you could finish many course in the faculty related to Maths , Programming and so on .

• Andrew Ng Coursera Course .

• https://www.coursera.org/learn/machine-learning

• https://medium.com/analytics-vidhya/python-implementation-of-andrew-ngs-machine-learning-course-part-1-6b8dd1c73d80

• Kaggle -- Browse Scikit-learn library - use their NN tool (and others if want)

Scikit-learn Documentation

https://blogs.mathworks.com/loren/201...

-- Kaggle: titanic (classification) and Boston housing prices competitions (regression).

• classification project

https://www.kaggle.com/c/titanic

• Regression project

https://www.kaggle.com/c/house-prices...

• Go deeper in Neural Networks -- Why NN?

Try to search about that as NN can do many things without need for the other models

it can act as classifier and regressor and many other reasons -- Deeper understanding

Its very mandatory matter --- Backpropagation

 • Lecture 10 - Neur...

!https://www.gstatic.com/youtube/img/watch/yt_favicon.png

 • 12a: Neural Nets

!https://www.gstatic.com/youtube/img/watch/yt_favicon.png

 • Lecture 4 | Intro...

!https://www.gstatic.com/youtube/img/watch/yt_favicon.png

Great practice :

--- On paper: Compute/Trace an example:

https://mattmazur.com/2015/03/17/a-st...

-- Reimplement NN from scratch --- Multi-class classification (softmax) --- Multi-label classification (sigmoid) --- Regression --- Try AE with your network (AutoEncoder) -- Learn dropout and implement it

-- FYI: NN/ML Arabic playlist:

 • Neural Networks I...

!https://www.gstatic.com/youtube/img/watch/yt_favicon.png

-- Maybe read NN chapter from a book

• Evaluation metrics https://towardsdatascience.com/metric...

-- Later: (when you start computer vision)

https://medium.com/@jonathan_hui/map-...

• AFTER you finish the above, do the following time split strategy

CNN and RNN

• 25% of your time for classical ML will be in -- kaggle projects / Kaggle blogs --

https://www.coursera.org/specializati...

-- Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

--

https://www.coursera.org/specializati...

 / @deeplearningai

!https://www.gstatic.com/youtube/img/watch/yt_favicon.png

-- [FYI]

https://classroom.udacity.com/courses...

-- Frameworks (TF/Pytorch) - study while learning -- Either learn 2D vision or nlp (later after work learn 2nd) --- Vision key problems: classification, detection, segmentation, pose estimation, object traction, action detection/recognition --- stanford courses ---- Computer Vision:

http://cs231n.stanford.edu/syllabus.html

---- NLP:

http://web.stanford.edu/class/cs224n/

• More on the road -- ML book, e.g. Bishop -- Understand more models, fields, more experience -- Field follow up: E.g. in Vision (CVPR, ICCV, ECCV) -- DL Experience: Paper-to-code skill -- Vision elements (later):

https://classroom.udacity.com/courses...

-- Abstracting skills

• Other ML areas (later) -- Generative models --- PGM, EM (GMM), VI, MCMC --- DL: VAE (variational autoencoder), GAN -(Study GAN)

https://medium.com/@jonathan_hui/gan-...

https://github.com/nashory/gans-aweso...

-- Reinforcement Learning

• What if you are also interested in competitive programming? -- ML is time-consuming to be good. In this case, divide your vacation to 50% competitive and 50% for ML