Machine Learning | Definition | Artificial Intelligence | Deep Learning | Applications | Algorithm | Supervised Learning | Unsupervised Learning | Reinforcement Learning

Machine Learning

   Machine Learning is a study of computer algorithms in which machines are trained to do a particular task. It is the field of Artificial Intelligence where it uses the data (training data), which is a stored in it to make predictions or decisions for some input. It gives the machines ability to learn and improve from its experiences without being programmed. 

   But it can be used for basic operations which require small data, for complex operations we use deep learning which doesn’t require data. It has its useful  application in computer vision and email filtering.

Three Ways of Machine Learning 

Supervised Machine Learning:

In this learning, the data present (called as predictor variables/features) in the machine are labeled with respect to the output data (target variable). This predictor variable make predictions according to the input data and goes on until it reaches the higher accuracy result with respect to the target variable.

Algorithms:

1. Linear regression

2. Logistic regression

3. Support Vector Machines

4. Naive Bayes

5. K-nearest neighbour algorithm

6. Random Forest Algorithm

Applications:

1. Bioinformatics

2. Quantitative structure

3. Database marketing

4. Handwriting recognition

5. Information retrieval

6. Learning to rank

7. Information extraction

8. Object recognition in computer vision

9. Optical character recognition

10. Spam detection

11. Pattern recognition

Unsupervised Machine Learning:

In this learning, the data present data present in the machine is unlabeled, i.e. it does not have the target variable to make predictions, so it uses the sequence of the input data to make certain predictions. For eg. The customers are categorized on the basis of the amount of products bought from company without knowing in advance the category.

Algorithm:

1. K-means Algorithm

2. Apriori Algorithm

3. Expectation–maximization algorithm (EM)

4. Principal Component Analysis (PCA)

 Application :

1. Human Behaviour Analysis

2. Social Network Analysis to define groups of friends.

3. Market Segmentation of companies by location, industry, vertical.

4. Organizing computing clusters based on similar event patterns and processes.

Reinforcement Machine Learning :

In this learning, the machine is programmed to perform some actions in the environment and in return it gets reward or punishment. On the basis of that reward or punishment, it programs itself to improve and perform further actions. This process of learning and improving goes on.

Algorithm:

1. Q-Learning Algorithm

2. State–action–reward–state–action Algorithm (SARSA)

3. Deep Q Network Algorithm (DQN)

4. Deep Deterministic Policy Gradient Algorithm (DDPG)

Application:

1. Resources management in computer clusters

2. Traffic Light Control

3. Robotics

4. Web System Configuration

5. Personalized Recommendations

6.Deep Learning

Facebook Google Twitter More

• Pinterest
• Linkedin
• Tumblr

August 05, 2020 Tags : Deep learning

Subscribe by Email

Follow Updates Articles from This Blog via Email