What I'm actually getting at is that AI/ML is now at the point where if one says "You need a Master's/PhD to do this sort of stuff" then it's like saying "You n

What I'm actually getting at is that AI/ML is now at the point where if one says "You need a Master's/PhD to do this sort of stuff" then it's like saying "You need a Computer Science degree to develop and debug software". The libraries, frameworks and operationalising of ML models is mature enough at this point that it's accessible to a wider audience than just people with research-level academic backgrounds. I'm not saying that your average web developer with no formal training can or even should be putting this kind of stuff in production. But someone with an undergraduate degree in Computer Science that's had a year or two of calculus and linear algebra and first year mathematical statistics should have no problem whatsoever in practice doing ML/AI. I mean look at this: from sklearn.model_selection import KFold # Define 10 fold cross-validation cv = KFold(n_splits=10) from sklearn.model_selection import GridSearchCV svm_model = GridSearchCV(svc_pipeline, param_grid=svc_parameters, scoring='f1_micro', cv=cv) svm_model.fit(X_train, y_train) A few lines and you're doing hyperparameter optimization on an SVM model with cross-validation. What a time to be alive.