**Pros**:
1. SVM can perform well on a range of datasets.
2. SVM is versatile: different kernel functions can be specified, or custom kernels can be defined for specific datatypes.
3. SVM works well for both low and high dimensional data.

**Cons**:
1. Efficiency decreases as training set size increases.
2. SVM needs careful normalization of input data and parameter tuning.
3. SVM provides no direct probability estimates (but can be estimated using e.g., Platt scaling).
4. It is difficult to interpret why a prediction was made.
5. It has high time complexity, which is $$O(n^2)$$, where $$n$$ represents the number of data points.

University of Michigan - Ann Arbor

Google

For dataset like p1.1, it's hard to use support vector classifier(soft margin classifier) since no matter where we put the classifier it will have a lot of misclassifications. 
In order to solve this problem, we can use support vector machine (SVM). The main idea behind SVM is we create a new y-axis and then square the value on x-axis. Now the value is two dimensional. We can then draw a support vector classifier that separates these two groups.

SVM is an extension of support vector classifier, which uses kernels to enlarge features in a certain way like what is depicted in the picture.

Support Vector Classifier vs Support Vector Machine

The kernel function in an SVM model calculates the similarity in the new feature space of two points in the original feature space.

The SVM Gaussian kernel function is more complicated than the linear kernel function model, and it is easy to overfit.

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