Types of Machine Learning

                            Machine Learning 

• Machine Learning is an application of artificial intelligence where a computer/machine learns from the past experiences (input data) and makes future predictions. It finds the pattern in the data , based on the pattern it gives the future predictions from the unseen data.
•  It is a way to understand the data and find the patterns in that.

Types of Machine Learning

 

 

 

 Supervised Machine Learning

• An algorithm learns from example data and associated target responses that can consist of numeric values or string labels. 
• Generally the algorithm should find the pattern how input and output is mapped

        Two types of Supervised Learning:

• Regression: 
• The problem is regression type when the output variable is real or continuous.
• Example : 
• Predicting salary of person based on past experience of person. 
• Predicting house price based on house details. 
• Classification:
• A classification problem is when the output variable is a categorical. 
• Example: 
• Predicting incoming mail is spam or not spam. 
• predicting whether loan should be accept or reject based on credit score of incoming loan application. 
• Algorithms:
• Linear Regression 
• Logistic Regression 
• k-Nearest Neighbor 
• Support vector Machine
• Decision Tress 
• Ensemble Learning 
• Naive Bayes Algorithm

Unsupervised Machine Learning

• ML learns from unlabeled data set then it is unsupervised ML. 
• The data set contain only inputs and no associated target responses. 
• Algorithms: 
• Clustering Algorithms 
• Association clustering

Reinforcement Learning

• Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. Reinforcement learning differs from the 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. 

        

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