What is Machine Learning?

Machine learning (ML) is a subfield of Artificial Intelligence (AI). ML is an umbrella term for any technique that enables computers to learn without being explicitly programmed. The various forms of ML include supervised learning, unsupervised learning, and reinforcement learning. This blog will focus on supervised machine learning models, which are also known as predictive models.

Types Of Machine Learning -

1) Supervised Learning :
Supervised learning is when the model is getting trained on a labelled dataset. Labelled dataset is one which have both input and output parameters. 

    Types of Supervised Learning:

     1)Classification

     2)Regression

Example of Supervised Learning Algorithms:

• Linear Regression
• Nearest Neighbor
• Guassian Naive Bayes
• Decision Trees
• Support Vector Machine (SVM)
• Random Forest

2) Unsupervised Learning :
It’s a type of learning where we don’t give a target to our model while training i.e. training model has only input parameter values. The model by itself has to find which way it can learn.

Training data we have to feed -

  a)Unstructured Data

  b)Unlabeled Data

Types of Unsupervised Learning:

     1)Clustering

     2)Association

Example of Unsupervised Learning algorithms: 

• K-Means Clustering
• DBSCAN – Density-Based Spatial Clustering of Applications with Noise
• BIRCH – Balanced Iterative Reducing and Clustering using Hierarchies
• Hierarchical Clustering

3) Reinforcement Learning :

Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.

Types of Reinforcement:

    1)Positive

    2)Negative