Introduction of Machine Learning.

What Is Machine Learning?

Machine learning is categorised as a subset of Artificial Intelligence (AI).

Machine learning :

Machine learning is a set of techniques to make computers better at doing things that humans can do better than machines.

OR also you can say that ( By book)

Machine learning is a branch of computer science with several machine learning algorithms that leans from Past Data and makes Predictions.

Types Of Machine Learning : 

1. Supervised Machine learning.
2. Unsupervised machine learning.
3. Reinforcement Machine learning.

1) Supervised Machine Learning:

• Supervised learning is the most deployed form of ML and also the easiest.

• Machines often learn from sample data with both an example input and Output.

• Example: Predict House prices

                    Predict Diabetic Patient 

•  Supervised learning problems include:

1. Classification

1. Regression

Classification:

• Classification algorithms are used when the target variable is categorical or discrete.

• A classification problem aims to determine what group a given input belongs to. For instance, a medical case where the possibilities are disease present / disease not present.

• Example: Predict Diabetic patient Dataset

Regression : 

• Regression Models are used when the target variable is Continuous.

•  Like classification, regression is also about inputs and corresponding outputs. But outputs for variety are typically discrete types (cat, dog), outputs for regression are a general number. In other words, it’s not a (0 or 1), but a sliding scale of possibility.

• Example: House price prediction.

2) Unsupervised Machine Learning:

• In unsupervised machine learning, the past data has no labels, in others there is no target variable.

• In unsupervised learning, the machine learns from data for which the outcomes are not known. It’s given input samples, but no output samples.

• For instance, imagine you have a set of documents that you would like to organize. For example, some documents may be about sports, others about history, and still others about the arts. Given only the set of documents, the objective is to automatically learn how to cluster them into types.

• Use Cases: Customer Segmentation.

• For simplicity's Sake, Consider only Two dimensions for the customers: Annual Income, Speeding Dcore (0-100).

3) Reinforcement Machine Learning: 

• Reinforcement learning is learning by interacting with its environment by producing actions and discovering errors or rewards, thus doesn't need past data.

• Reinforcement learning differs from supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself,

whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of a training dataset, it is bound to learn from its experience.

Main points in Reinforcement learning –  

• Input: The input should be an initial state from which the model will start
• Output: There are many possible outputs as there are a variety of solutions to a particular problem
• Training: The training is based upon the input, The model will return a state and the user will decide to reward or punish the model based on its output.
• The model keeps continues to learn.
• The best solution is decided based on the maximum reward.

                                 _Thank_You_

Dishant Kharkar