Inroduction To ML

Machine learning is a growing technology which enables computers to learn automatically from past data.
Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Currently, it is being used for various tasks such as image recognition, speech recognition, email filtering, Facebook auto-tagging, recommender system, and many more.
There has been a significant increase in the number of Machine Learning enthusiasts across the globe.

Types of Machine Learning

  • Supervised ML
  • Unsupervised ML
  • Reinforcement ML

1) Supervised ML :
It is the types of machine learning in which machines are trained using well “labelled” training data, and on basis of that data, machines predict the output.
• In supervised learning, models are trained using labelled dataset.
• Model learns about each type of data.
• Once the training process is completed, the model is tested on the basis of test data (a subset of the training set).
• then it predicts the output.

Types of Supervised ML

  • Regression
  • Classification

Regression algorithms are used if there is a relationship between the input variable and the output variable. It is used for the prediction of continuous variables, such as Weather forecasting, Market Trends, etc. Below are some popular Regression algorithms which come under supervised learning:
• Linear Regression
• Regression Trees
• Non-Linear Regression
• Bayesian Linear Regression
• Polynomial Regression

Classification algorithms are used when the output variable is categorical, which means there are two classes such as Yes-No, Male-Female, True-false, etc.
Spam Filtering,
• Random Forest
• Decision Trees
• Logistic Regression
• Support vector Machines

2) Unsupervised ML :
It is a machine learning technique in which models are not supervised using training dataset.
Models itself find the hidden patterns and insights from the given data.
Unsupervised learning cannot be directly applied to a regression or classification problem because unlike supervised learning, we have the input data but no corresponding output data. The goal of unsupervised learning is to find the underlying structure of dataset, group that data according to similarities, and represent that dataset in a compressed format.

Types of Unsupervised ML

  • Clustering
  • Association

• It is a method of grouping the objects into clusters such that objects with most similarities remains into a group
• Cluster analysis finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities.

• It is used for finding the relationships between variables in the large database.
• people who buy X item (suppose a bread) are also tend to purchase Y (Butter/Jam) item.
• Association rule makes marketing strategy more effective.

3) Reinforcement ML
It is technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions.
For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback.

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