Hands-On Machine Learning with Scikit-Learn and PyTorch

Overview

Machine learning is the science (and art) of programming computers so they can learn from data
A computer program is said to learn from experience $E$ with respect to some task $T$ and some performance measure $P$ , if its performance on $T$ as measured by $P$ improves with experience $E$ — Tom Mitchel, 1997
The examples that the system learns from are called the training set.
Each training example is called a training instance (or sample).
The part of a machine learning system that learns and makes predictions is called a model.
Digging into large amounts of data to discover hidden patterns is called data mining

ML is great for:
- Problems for which existing solutions require a lot of work and maintenance such as long list of rules (an ML model can often simply code and perform better than the traditional approach).
- Complex problems for which using a traditional approach yields no good solution (the best ML techniques can perhaps find a solution).
- Fluctuating environment (an ML system can easily be retrained on new data, always keeping it up to date)
- Getting insights about complex problems and large amounts of data.

ML Systems can be classified into broad categories based on the following criteria:
- Training Supervision: supervised, unsupervised, semi-supervised, self-supervised, reinforcement
- Online learning vs Batch learning: whether they can learn incrementally on the fly.
- Instance-based versus Model-based learning: whether they work by simply comparing new data points to known data points

Supervised learning: the training set you feed to the algorithm includes the desired solutions, called labels. Typical supervised learning tasks include:
- Classification: Model is trained with training instances and their categorical labels/class. The used for predicting the class or the categorical labels of the

Note: This is my personal assessment based on how much the book influenced my thinking or provided practical value.