My learning from a ML course

I am following this course and these notes are some of the main points that i think are important . Please refer to this course by Andrew NG on coursera
https://www.coursera.org/learn/machine-learning/home/welcome

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what is machine learning ?

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.

Example :-
https://www.coursera.org/learn/machine-learning/lecture/Ujm7v?t=157

Let's say your email program watches which emails you do or do not mark as spam. So in an email client like this, you might click the Spam button to report some email as spam but not other emails. And based on which emails you mark as spam, say your email program learns better how to filter spam email. What is the task T in this setting?

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Types of Learning Algorithms
There are two types of learning algorithm

1. supervised learning
2. unsupervised learning

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Supervised Learning
Informal definition
Supervised Learning refers to the fact that we gave the algorithm a data set in which the, called, "right answers" were given.The task of the algorithm was to just produce more of these right answers

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 further has 2 types

• Regression (continuous)
• Classification (discrete)

Predicting the price of the house from a historical set of data is a continuous value output . This type of supervised learning is called Regression

Note: the blue and the pink line are two different predicting algorithms

Classification problem where the goal is to predict a discrete value output. like classifying whether a given tumor is malignant or benign

Note: classification can be dependent on more than one feature.For example a tumor can be predicted as benign or malignant on the basis of age, tumor size , clump of thickness etc etc .

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Unsupervised learning
Unsupervised learning defines two or more clusters. Video explaining Unsupervised learning https://www.coursera.org/learn/machine-learning/lecture/olRZo/unsupervised-learning

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