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Artificial Intelligence and Machine Learning

Artificial Intelligence refers to intelligence displayed by machines that
simulates human and animal intelligence.

Artificial Intelligence in Practice

Relationship between Artificial Intelligence and Machine Learning

Machine Learning is an approach or subset of Artificial Intelligence that is
based on the idea that machines can be given access to data along with
the ability to learn from it.

The capability of Artificial Intelligence systems to learn by extracting patterns from data is known as Machine Learning.

Features of Machine Learning

Machine Learning is computing intensive and generally requires a large
amount of training data. It involves repetitive training to improve the learning and decision making of algorithms.
As more data gets added, Machine Learning training can be automated for
learning new data patterns and adapting its algorithm.
Example: Learning from new spam words or new speech (also called as incremental learning)

Traditional Programming vs. Machine Learning Approach

Traditional programming relies on hard coded rules. Machine Learning relies on learning patterns based on sample data.

Relationship between Data Science and Machine Learning

Data Science and Machine Learning go hand in hand. Data Science helps evaluate data for Machine Learning algorithms. Data science is the use of statistical methods to find patterns in the data. Statistical machine learning uses the same math and techniques as data science. These techniques are integrated into algorithms that learn and improve on their own. Machine Learning facilitates Artificial Intelligence as it enables machines to learn from the patterns in data.

Machine Learning Techniques

Machine Learning uses a number of theories and techniques from Data Science:

Machine Learning Algorithms

Machine Learning algorithms involving unlabelled data, or unsupervised
learning, are more complicated than those with the labelled data or
supervised learning.
Machine Learning algorithms can be used to make decisions in subjective
areas as well.
Examples:
Logistic Regression can be used to predict which party will win at the
ballots.
Naïve Bayes algorithm can separate valid emails from spam.
Machine Learning can learn from labelled data (known
as supervised learning) or unlabelled data (known as unsupervised learning).

Applications of Machine Learning

Artificial intelligence and Machine learning is being increasingly used in various functions such as:

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