Introduction to Machine Learning GATE DA

Artificial intelligence (AI) and machine learning are often used interchangeably, but machine learning is a subset of the broader category of AI.

Put in context, artificial intelligence refers to the general ability of computers to emulate human thought and perform tasks in real-world environments, while machine learning refers to the technologies and algorithms that enable systems to identify patterns, make decisions, and improve themselves through experience and data. 

Computer programmers and software developers enable computers to analyze data and solve problems — essentially, they create artificial intelligence systems — by applying tools such as:

• machine learning
• deep learning
• neural networks
• computer vision
• natural language processing

Below is a breakdown of the differences between artificial intelligence and machine learning as well as how they are being applied in organizations large and small today.

What Is Artificial Intelligence?

Artificial Intelligence is the field of developing computers and robots that are capable of behaving in ways that both mimic and go beyond human capabilities. AI-enabled programs can analyze and contextualize data to provide information or automatically trigger actions without human interference.

Today, artificial intelligence is at the heart of many technologies we use, including smart devices and voice assistants such as Siri on Apple devices. Companies are incorporating techniques such as natural language processing and computer vision — the ability for computers to use human language and interpret images ­— to automate tasks, accelerate decision making, and enable customer conversations with chatbots.

What Is Machine Learning?

Machine learning is a pathway to artificial intelligence. This subcategory of AI uses algorithms to automatically learn insights and recognize patterns from data, applying that learning to make increasingly better decisions.

By studying and experimenting with machine learning, programmers test the limits of how much they can improve the perception, cognition, and action of a computer system.

Deep learning, an advanced method of machine learning, goes a step further. Deep learning models use large neural networks — networks that function like a human brain to logically analyze data — to learn complex patterns and make predictions independent of human input.

How Companies Use AI and Machine Learning

To be successful in nearly any industry, organizations must be able to transform their data into actionable insight. Artificial Intelligence and machine learning give organizations the advantage of automating a variety of manual processes involving data and decision making.

By incorporating AI and machine learning into their systems and strategic plans, leaders can understand and act on data-driven insights with greater speed and efficiency.

AI in the Manufacturing Industry

Efficiency is key to the success of an organization in the manufacturing industry. Artificial intelligence can help manufacturing leaders automate their business processes by applying data analytics and machine learning to applications such as the following:

• Identifying equipment errors before malfunctions occur, using the internet of things (IoT), analytics, and machine learning
• Using an AI application on a device, located within a factory, that monitors a production machine and predicts when to perform maintenance, so it doesn’t fail mid-shift
• Studying HVAC energy consumption patterns and using machine learning to adjust to optimal energy saving and comfort level

AI and Machine Learning in Banking

Data privacy and security are especially critical within the banking industry. Financial services leaders can keep customer data secure while increasing efficiencies using AI and machine learning in several ways:

• Using machine learning to detect and prevent fraud and cybersecurity attacks
• Integrating biometrics and computer vision to quickly authenticate user identities and process documents
• Incorporating smart technologies such as chatbots and voice assistants to automate basic customer service functions

AI Applications in Health Care

The health care field uses huge amounts of data and increasingly relies on informatics and analytics to provide accurate, efficient health services. AI tools can help improve patient outcomes, save time, and even help providers avoid burnout by:

• Analyzing data from users’ electronic health records through machine learning to provide clinical decision support and automated insights
• Integrating an AI system that predicts the outcomes of hospital visits to prevent readmissions and shorten the time patients are kept in hospitals
• Capturing and recording provider-patient interactions in exams or telehealth appointments using natural-language understanding

Here are some important definitions related to AI and ML

 Q) What do you mean by data set?

A data set is an ordered collection of data. As we know, a collection of information obtained through observations, measurements, study, or analysis is referred to as data.

 Q) What are different types of data sets used in ML?

 There are mainly three different types of data sets available: Training data set, validation data set, and testing data set.

Q) What do you mean by labelled and unlabelled data?

Labeled data is data that has been assigned a category or label. For example, an image of a cat labeled as "cat" is labeled data. Labeled data is used to train supervised learning machine learning models.

Unlabeled data is data that has not been assigned a category or label. For example, an image of an animal that has not been labeled is unlabeled data. Unlabeled data can be used to train unsupervised learning machine learning models.