Fix a few typos

Author: fuglede

notebooks/05.07-Support-Vector-Machines.ipynb

@@ -201,7 +201,7 @@
  "### Fitting a support vector machine\n",
  "\n",
  "Let's see the result of an actual fit to this data: we will use Scikit-Learn's support vector classifier to train an SVM model on this data.\n",
- "For the time being, we will use a linear kernel and set the ``C`` parameter to a very large number (we'll discussthe meaning of these in more depth momentarily)."
+ "For the time being, we will use a linear kernel and set the ``C`` parameter to a very large number (we'll discuss the meaning of these in more depth momentarily)."
  ]
  },
  {
@@ -998,7 +998,7 @@
  "However, SVMs have several disadvantages as well:\n",
  "\n",
  "- The scaling with the number of samples $N$ is $\\mathcal{O}[N^3]$ at worst, or $\\mathcal{O}[N^2]$ for efficient implementations. For large numbers of training samples, this computational cost can be prohibitive.\n",
- "- The results are strongly dependent on a suitable choice for the softening parameter $C$. This must be carefully chosen via cross-validation, which can be expensive as datasets grow in size\n",
+ "- The results are strongly dependent on a suitable choice for the softening parameter $C$. This must be carefully chosen via cross-validation, which can be expensive as datasets grow in size.\n",
  "- The results do not have a direct probabilistic interpretation. This can be estimated via an internal cross-validation (see the ``probability`` parameter of ``SVC``), but this extra estimation is costly.\n",
  "\n",
  "With those traits in mind, I generally only turn to SVMs once other simpler, faster, and less tuning-intensive methods have been shown to be insufficient for my needs.\n",