To summarize, Machine Learning is great for:
Problems for which existing solutions require a lot of hand-tuning or long
lists of rules: one Machine Learning algorithm can often simplify code and
perform better.
Complex problems for which there is no good solution at all using a
traditional approach: the best Machine Learning techniques can find a
solution.
Fluctuating environments: a Machine Learning system can adapt to new
data.
Getting insights about complex problems and large amounts of data.
Types of Machine Learning Systems
There are so many different types of Machine Learning systems that it is useful to
classify them in broad categories based on:
Whether or not they are trained with human supervision (supervised,
unsupervised, semisupervised, and Reinforcement Learning)
Whether or not they can learn incrementally on the fly (online versus batch
learning)
Whether they work by simply comparing new data points to known data
points, or instead detect patterns in the training data and build a predictive
model, much like scientists do (instance-based versus model-based learning)
These criteria are not exclusive; you can combine them in any way you like. For
example, a state-of-the-art spam filter may learn on the fly using a deep neural
network model trained using examples of spam and ham; this makes it an online,
model-based, supervised learning system.
Let’s look at each of these criteria a bit more closely.
Supervised/Unsupervised Learning
Machine Learning systems can be classified according to the amount and type of
supervision they get during training. There are four major categories: supervised
learning, unsupervised learning, semisupervised learning, and Reinforcement
Learning.
SUPERVISED LEARNING
In supervised learning, the training data you feed to the algorithm includes the
desired solutions, called labels (Figure 1-5).
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