I'm currently finishing up Andrew Ng's Coursera course, taught in MATLAB/Octave, but I'm looking to code in Python. The course is an introduction into how some algorithms work from scratch. These include: Linear/Logistic Regression, gradient descent, Support Vector Machines, Neural Networks, K-means clustering, anomaly detection, Gaussian distribution. (Decision trees/random forests aren't covered so I have to learn those).
- Any advice on implementing this theoretical knowledge into Python? Is it necessary/recommended to recode the MATLAB algorithms from scratch in Python, or instead should I jump straight into learning Python libraries (sk-learn for now with pandas, matplotlib, etc.) and start projects?
- Any project recommendations for my level? Certain concepts I should focus on now? Where to next? I know Python relatively well and have completed tutorials on numPy, Pandas, matplotlib, and sk-learn (very basic).
Next I plan on studying Harvard's CS109 data science course and taking Andrew Ng's Deep Learning Specialization on Coursera.
My ideal goal (a long journey) is to become a machine learning engineer and get into AI and deep learning.
Any advice is welcome! Thanks a lot.