Machine learning basics-part 1

Machine learning is a subfield inside Artificial Intelligence and the most dominating one in current scenerio.

IBM describes Machine learning as -

"Machine Learning is a branch of AI and Computer Science which focuses on the use of data and algorithms to imitate the way that humans learn , gradually improving the accuracy."

Another cool definition that I found on the internet is -

"It is a branch of AI that enables computers to "self-learn" from training data and improve overtime without being explicitly programmed."

Machine Learning is further categorised as -

1.Supervised Learning

2.Unsupervised Learning

3.Reinforcement Learning

Out of all the economy that ML has generated , 99% percent is through Supervised Learning .

Let's take a look at Supervised Learning-

IBM describes it as follows-

It is defined by use of labeled datasets to train algorithms that classify data or predict outcomes accuarately.As input data is fed into the model , it adjusts its weights until the model has been fitted appropriately , which occurs as a part of cross validation process. Supervised Learning helps organizations solve for a variety of real-world problems at large scale such as classifying spam in a seperate folder from your inbox.

A model is an algorithm which feeds on data .In supervised learning , the training of model happens as follows -the model is provided with lots of data , for each data item it gives its own prediction then the predicted result is compared with actual result and the "cost"(error) us determined . The weights in model are adjusted such that the cost function is minimized or the model "fits" the training data in vest possible way . So , based on this training it provides best possible prediction for the test data .

It essentially mimicks the way humans learn .For example , when preparing for a exam , you feed yourself with more and more practice questions (training data ) so that you give best prediction in the exam (test data), you are the model .

The two most basic algorithms of Supervised Learning are -

1.Linear Regression

2.Logistic Regression

We will take a look at them one by one in next blog.