Hey guys, I am a soft-ware developer. This year I want to learn more about Machine Learning(ML). So, I will post everything I have learned from the courses, videos, blog posts of ML and share with you guys. If You found any mistakes on any of my posts, feel free to comment on it so we can grow together.
What is Machine learning？
In a short conclusion of mine, it is way to teach a machine how to learn by itself through the data it collected. Personally, I think it is a super power that humans can have in the future. Machine learning is to create a smart machine which grows by us feeding data to it. It can be really dangerous and helpful.
Two definitions of Machine Learning are offered.
- Arthur Samuel described it as: “the field of study that gives computers the ability to learn without being explicitly programmed.” This is an older, informal definition.
- Tom Mitchell provides a more modern definition: “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.” Stanford University Online
Two Ways of learning.
1. supervised Learning
In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
Supervised learning problems are categorized into “regression” and “classification” problems.
In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function.
In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories
(a) Regression - Given a picture of a person, we have to predict their age on the basis of the given picture
(b) Classification - Given a patient with a tumor, we have to predict whether the tumor is malignant or benign. – Stanford University Online
2. Unsupervised Learning
Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.
We can derive this structure by clustering the data based on relationships among the variables in the data.
With unsupervised learning there is no feedback based on the prediction results.
Example:
Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.
Non-clustering: The “Cocktail Party Algorithm”, allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).– Stanford University Online
From a personal definition after the first day of learning it, I conclude that the differences between these two are, supervised learning is to give a structure of a bunch of data. Unsupervised learning is that to give machine a whole bunch of data, let machine itself to find the rules, structures.
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