Getting started with Machine learning
Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed Arthur Samuel
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E -Tom Mitchell
Example: playing checkers.
E = the experience of playing many games of checkers
T = the task of playing checkers.
P = the probability that the program will win the next game
Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to better filter spam. What is task T in this setting?
Well, Classifying emails as spam or not spam will be the task.
Examples of machine learning
- Database mining- Large datasets from the growth of automation/web. E.g., Web click data, medical records, biology, engineering
- Applications can’t program by hand -E.g., Autonomous helicopter, handwriting recognition, most of Natural Language Processing (NLP), Computer Vision.
- Self-customizing programs E.g., Amazon, Netflix product recommendations
- Understanding human learning (brain, real AI).
Moving forward -
Machine learning algorithms are broadly classified into -
- Supervised learning
- Unsupervised learning
There are other subdivisions such as Reinforcement learning, recommender systems, etc which we will be studying later.
Disclaimer — This series is based on the notes that I created for myself based on various books and videos I’ve read or seen , so some of the text could be an exact quote from some book/videos out there.
