Business Application of Machine Learning (Chapter 1)

 

I. Artificial Intelligence (AI):

Ability of machines to mimic human intelligence.

II. Data Science:

Field that extracts insights from data using statistics and computing.

III. Main concepts of Big Data:

Volume, Velocity, Variety (3Vs).

IV. Relationship of Big Data to Data Science:

Big Data provides large datasets; Data Science analyzes them.

V. Machine Learning:

Machines learning from data without explicit programming.

VI. Deep Learning:

Machine learning using multi-layer neural networks.

VII. Relationship between AI, ML, and Data Science:

AI is the broad field; ML is a subset of AI; Data Science uses ML for analysis.

VIII. Components of Natural Intelligence:

Learning, reasoning, memory, perception, problem-solving.

IX. Supervised Learning:

Learning using labeled data.

X. Unsupervised Learning:

Learning using unlabeled data.

XI. Characteristics of Early AI Adopters:

Innovative, tech-savvy, risk-takers.

Exam-oriented answers:

I. Artificial Intelligence (AI):

Artificial Intelligence is the branch of computer science that enables machines to think, learn, and make decisions like humans.

II. Data Science:

Data Science is an interdisciplinary field that uses statistics, programming, and domain knowledge to analyze data and extract useful information.

III. Main concepts of Big Data:

Big Data is defined by the 3Vs: Volume (large data size), Velocity (speed of data generation), and Variety (different data types).

IV. Relationship of Big Data to Data Science:

Big Data provides massive and complex datasets, while Data Science processes and analyzes this data to gain insights.

V. Machine Learning:

Machine Learning is a subset of AI where systems learn patterns from data and improve performance without explicit programming.

VI. Deep Learning:

Deep Learning is a subset of Machine Learning that uses neural networks with multiple layers to process complex data.

VII. Relationship between AI, ML, and Data Science:

AI is the broader concept of intelligent machines, ML is a method used in AI, and Data Science applies ML techniques to analyze data.

VIII. Components of Natural Intelligence:

Natural intelligence includes learning, reasoning, perception, memory, and decision-making abilities of humans.

IX. Supervised Learning:

Supervised learning is a machine learning technique where models are trained using labeled input and output data.

X. Unsupervised Learning:

Unsupervised learning is a technique where models find patterns in data without predefined labels.

XI. Characteristics of Early AI Adopters:

Early AI adopters are innovative, open to change, willing to take risks, and invest in new technologies.