# Introduction to Machine Learning

“Computers are able to see, hear and learn. Welcome to the future.”

Definition :

• Machine Learning is a study of various types of algorithms of Artificial Intelligence that enabled self-learning from data without explicitly programmed.

Purpose :

• The primary goal of ML is to observe the given problem or a dataset and determine the hidden patterns and relationships and extract details/information which helps in data-driven decision making and provides insights.

Any dataset has some terminologies that help us while choosing an appropriate ML model.

There are mainly three types of learnings:

1. Supervised Learning

# Supervised Learning :

• A type of machine learning called supervised learning uses pre-labeled data to train a machines or models, which predicts the output based on the labelled data. Labeled data means that the input data can already be tagged with the desired output.

Working:

Example:

Suppose we have a dataset that contains the number of hours of student study as well as corresponding percentages they have got. So, we can say that we are given input/features(Hours) and output/label/response(Percentage).

Which means we can train our model by hours and percentage. For testing purposes, we can take new data as an input and check the corresponding output and determine whether it is accurate or not.

Supervised ML can also be further categorized into two group

1. Regression (It is used when data is continuous):
It is an optimal method to distinguish the relationship between a dependent and one or more dependent variables.

Steps involved in Supervised Learning:

1. Determine the type of training dataset or identify the domain of problem.

Supervised Learning algorithms:

• Linear Regression

Generalization refers to your model’s ability to adapt properly to new, previously unseen data, drawn from the same distribution as the one used to create the model.

# Unsupervised Learning :

• This is a type of machine learning in which models are trained using the unlabeled dataset like images, audio, videos, x-rays, etc.

“Unsupervised learning is a type of machine learning in which models are trained using unlabeled dataset and are allowed to act on that data without any supervision.”

Working:

Unsupervised ML can also be further categorized into two group

1. Clustering:
A simple way of grouping the data points into various clusters. The objects or points having similarities remain in a group.

Unsupervised Learning algorithms:

• K-means clustering

# Reinforcement Learning :

• It is a feed-back based machine learning technique in which agent learns to behave in an environment by performing the actions and seeing the results of actions. For good action agent gets positive feedback and for dull action agent gets negative feedback or penalty.
• In reinforcement learning the agent learns automatically using feedbacks without any labeled data unlike supervised learning. Agent interacts directly with the environment and explore it by itself.

“Reinforcement Learning is a type of machine learning method where an intelligent agent or computer program interact with the environment and learn to act within that”.

• Feedback that agent gets after performing an action from the environment is called “Reward Signal”. Reinforcement learning (RL) deals with the ability of learning the associations between stimuli, actions, and the occurrence of pleasant events, called rewards, or unpleasant events called punishments.

Terms used in Reinforcement Learning

• Agent(): An entity that can perceive the environment and act upon it.

Elements of Reinforcement Learning

1. Policy
A policy can be defined as a way how an agent behaves at a given time. Policy is a core element of the RL, since it is the only thing that defines how the agent behaves.

Applications of Reinforcement Learning

1. Self-driving vehicles

Example
A mobile trash collecting robot decides whether it should enter a new room in search of more trash to collect or start to find a way to get back to it battery recharge station. This decision is made based on how quickly and easily it has been able to find the recharge in past. This can be done by reinforcement learning algorithm.

Applications of Machine Learning

1. Image and Speech recognition.

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## More from Prakhar Patel

Hello, I’m a computer student passionate about data science. I believe the best way to broaden our knowledge is to share it with people.

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Hello, I’m a computer student passionate about data science. I believe the best way to broaden our knowledge is to share it with people.