Training(chapter 4)
This is covered more by professor Andrews Ng, so I will study cs229 after this one.
SVM
SVM has hard classification and soft classification. Hard Classification is linearly separable, soft classification gives margin violations.
The python code is simple
svm_clf = Pipeline((
("scaler", StandardScaler()),
("linear_svc", LinearSVC(C=1, loss="hinge")), #applies Stochstic Gradient Descent rather than batch
))
svm_clf.fit(X_scaled, y)
and can add polynomial features by
("poly_features", PolynomialFeatures(degree=3))
and then do linear SVC
Adding polynomial features is simple to implement and can work great with all sorts of Machine Learning algorithms (not just SVMs), but at a low polynomial degree it cannot deal with very complex datasets, and with a high polynomial degree it creates a huge number of features, making the model too slow.
Gaussian Radial Basis
("svm_clf", SVC(kernel="rbf", gamma=5, C=0.001))
Computational complexity
linear SVC mn SGD mn SVC m^2~m^3 * n
Decision and prediction
decision function = 0 if theta^T . x < 0 and vise versa The goal is to minimize theta conditioned on >1 on positive cases and <1 on negative instances
Hinge Loss
equivalent to max(0, 1 – t)

Previous
Hands On Machine Learning with Scikit and Tensorflow(II) 
Next
Hands On Machine Learning with Scikit and Tensorflow(IV)