Machine Learning-

 

Learning is a continuous process of improvement over experience.

 

Machine learning is building machines that can adapt and learn from experience without being explicitly programmed.

 

In machine learning,

• There is a learning algorithm.
• Data called as training data set is fed to the learning algorithm.
• Learning algorithm draws inferences from the training data set.
• It generates a model which is a function that maps input to the output.

 

 

Machine Learning Applications-

 

Some important applications of machine learning are-

• Spam Filtering
• Fraudulent Transactions
• Credit Scoring
• Recommendations
• Robot Navigation

 

Machine Learning Algorithms-

 

There are three types of machine learning algorithms-

 

 

1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

 

1. Supervised Learning-

 

In this type of machine learning algorithm,

• The training data set is a labeled data set.
• In other words, the training data set contains the input value (X) and target value (Y).
• The learning algorithm generates a model.
• Then, new data set consisting of only the input value is fed.
• The model then generates the target value based on its learning.

 

 

Example-

 

Consider a sample database consisting of two columns where-

• The first column specifies mails.
• The second column specifies whether those emails are spam or not.

 

Mails (X)	IsSpam (Y)
Mail-1	Yes
Mail-2	No
Mail-3	No
Mail-4	No

 

In this training data set, emails categorized as spam or not are done by a supervisor’s knowledge.

So, it is supervised learning algorithm.

 

Applications-

 

Some real-life applications are-

• Spam Filtering
• House Price Prediction
• Credit Scoring (high risk or a low risk customer while lending loans by the banks)
• Face Recognition etc

 

Types of Supervised Learning Algorithm-

 

There are two types of supervised learning algorithm-

 

 

1. Regression
2. Classification

 

Regression-

 

Here,

• The target variable (Y) has continuous value.
• Example- house price prediction

 

Classification-

 

Here,

• The target variable (Y) has discrete values such as Yes or No, 0 or 1 and many more.
• Example- Credit Scoring, Spam Filtering

 

2. Unsupervised Learning-

 

In this type of machine learning algorithm,

• The training data set is an unlabeled data set.
• In other words, the training data set contains only the input value (X) and not the target value (Y).
• Based on the similarity between data, it tries to draw inference from the data such as finding patterns or clusters.

 

 

Applications-

 

Some real-life applications are-

• Document Clustering
• Finding fraudulent transactions

 

3. Reinforcement Learning-

 

In this type of machine learning algorithm,

• The agent acts in an environment in order to maximize the rewards and minimize the penalty.
• Unlike supervised learning, no data is provided to the agent.
• The agent itself takes action or sequence of actions whether right or wrong to perform a task and learn from the experience.

 

Applications-

 

Some real-life applications are-

• Game Playing
• Robot Navigation

 

To gain better understanding about Machine Learning & its Algorithms,

Watch this Video Lecture

 

Next Article- Machine Learning Workflow

 

Get more notes and other study material of Machine Learning.

Watch video lectures by visiting our YouTube channel LearnVidFun.