Types Of Machine Learning???

There are three types of machine learning.

1. Supervised
2. Unsupervised
3. Reinforcement

Supervised Learning:

Supervised Learning works with the labeled data and here the output data patterns are known to the system. For example, if we want our Ml model to predict whether fruits are apples or bananas, the label would take the values of “apple” or “banana” and the feature set could include weight, length, width, and any other relevant measurements of the fruits that are available.

Supervised Learning deals with two main tasks Regression and Classification.

Algorithms of Supervised Learning:

1. Linear Regression
2. Logistic Regression
3. SVM
4. Random Forest
5. KNN and many more.

Application of Supervised Learning:

1. Risk Evaluation
2. Forecast Sales
3. Disease.

Unsupervised Learning:

Unsupervised Learning is a technique that determines patterns and associations in unlabeled data. This technique is often used to create groups and clusters. Unsupervised learning is often used for exploratory analysis and anomaly detection because it helps to see how the data segments relate and what trends might be present.

Unsupervised Learning deals with clustering and associative rule mining problems. Therefore, Unsupervised Learning explores patterns and predicts the output.

Algorithms of Unsupervised Learning:

1. K-Means Clustering
2. C-Means
3. Principal Component Analysis (PCA)
4. Autoencoding

Application of Unsupervised Learning:

1. Recommendation System
2. Anomaly Detection

Reinforcement Learning:

Reinforcement learning is all about making decisions sequentially. In simple words, we can say that the output depends on the state of the current input and the next input depends on the output of the previous input. In Reinforcement learning decision is dependent, So we give labels to sequences of dependent decisions. For example, Reinforcement learning can be applied to the control of a simple machine like a car driving down a winding road.

Reinforcement Learning deals with exploitation or exploration.

Algorithms of Reinforcement Learning:

1. Q-Learning
2. SARSA

Applications of Reinforcement Learning:

1. Robotics
2. Autonomous Driving
3. Gaming
4. Health Care