Machine Learning - 1

Machine Learning = Science of getting computer (machine) to learn without being explicitly programmed


How do machines learn? What are the major types of machine learning problems.


Supervised Learning: 

Using data supplied for learning (supervised learning). In this case , algorithm uses previous data to predict the outcome for new data. So we feed in "right answers" to the algorithm.

Using supervised learning algo we can solve a regression or a classification problem .

Predicting house prices based on given sample data (let's say a mapping of area and actual price) will be a regression problem.

Regression = Predicting continuous value output.

Predicting the probability of cancer being malignant or benign based on dataset (tumor size and cancer type) for these classes or types of cancer will be a classification problem.

Classification = Predicting a discrete output (categorization into different classes)

In above examples, we had only a feature or parameter based on which we were predicting outcomes. But there will many parameters in real world machine learning problems.

Even for house pricing example, we can add parameters like age of house, nearest market etc.

So, essentially in case of supervised learning, algorithm is explicitly told what is the so called right answer.


Unsupervised Learning: 

In unsupervised learning problems, algorithms make sense of the data set on their own - find if there is any structure in the data set. The algorithm will segregate data in different clusters (hence cluster algorithm).

Once of the example here will be grouping different types of people together given a set of DNA microarray data.



List of machine learning posts :

1.  Nuts: Machine Learning -1 , Basics of machine learning
2.  Machine Learning - 2 : Learning Algorithms



References -

1. https://www.coursera.org/learn/machine-learning
2. http://cs229.stanford.edu