What is Machine Learning?

What is machine learning

What is Machine Learning?

Objectives

  • Introduction to Machine Learning
  • Machine Learning Techniques
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Algorithms in Machine Learning

Introduction to Machine Learning

“Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.”

Machine Learning Tutorial For Beginners | GangBoard

Machine Learning: When do we use it?

A simple problem can always be solved by designing simple code in any particular Language like Python, R, etc. But in case of very complex problems, Machine Learning comes into the picture. We can also use it when the problem involves a huge amount of data where there isn’t any formula or equation existing to solve.

For Example:

  • Face recognition
  • Speech recognition.
  • Fraud detection
  • Demand forecasting

Machine Learning Techniques

There are three types of Machine Learning techniques. They are:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Machine Learning Techniques

Supervised Learning:

Supervised Learning is a type of Machine Learning used to learn models with a labeled training data.

When do we use Supervised Leaning?

We opt Supervised Learning technique when we know what we want the algorithm/model to do which means we know exactly what the Target element here is. Few examples that both model and us know the answer to are used to train the model which in turn helps in predicting others examples where we/model doesn’t know answers to.

For Example:

  • Face recognition,
  • Diagnosis,
  • Email spam detector etc.

Types of Supervised Learning:

There are two types of Supervised Learning.

  • Classification
  • Regression

Supervised Machine Learning Algorithm

Unsupervised Learning:

Unsupervised Learning is a type of Machine Learning that always feed on unlabeled dataset.

When do we use Unsupervised Leaning?

Generally, we opt Unsupervised Learning technique when we don’t know what exactly we want the algorithm to do. In this case, we have to be discovering patterns from the unlabeled data inputted and predict the results informatively.

For Example: GoogleNews search (Clusters) etc.

Types of Unsupervised Learning:

  • Clustering
  • Anomaly Detection
  • Recommendation System

Clustering

Anomaly Detection

Recommendation System:

Recommendation Systems

Reinforcement Learning:

Reinforcement learning system is mainly known for multitasking. It’s also called as Trial and Error technique as it uses trial and error method in learning the model. Once the model is been learnt, it’s used in predicting the next steps in upcoming iterations.

The model will return a state and the User/Agent will decide to Reward or Punish the model based on the output generation.

Reward – Positive Feedback

Punish – Negative Feedback

Reinforcement Learning is also known as Feedback BasedLearning.

Reinforcement Learning

Machine Learning Algorithms

  • Linear Regression
  • Logistic Regression
  • K Nearest Neighbours
  • Naïve Bayes
  • Decision Tree
  • Support Vector Machine
  • Artificial Neural Networks
  • K Means Clustering
  • DBSCAN Clustering
  • Principal Component Analysis
  • And many more

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