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Machine Learning Process
Machine Learning
Supervised Unsupervised Reinforcement
Task Driven Data Driven Learn From
(Predict Next Value) (Identify Clusters) (Mistakes)
Machine learning algorithms can be used for a wide range of applications, such as image and speech recognition,
natural language processing, fraud detection, predictive analytics, and autonomous vehicles. For example, machine
learning can be used to develop chatbots that can understand and respond to queries in a human language or to
train self-driving cars to recognize and respond to different driving scenarios. Moreover, machine learning can be
used to create personalized recommendations for users based on their past behavior. It can predict which products
or services a user is most likely to be interested in. The success of machine learning models depends on the quality
and quantity of the data used for training. The more data that is available, the more accurate and robust the model
is likely to be.
Supervised learning: Training a model on labelled data to make predictions or classifications based on new,
unlabelled data.
Unsupervised learning: Training a model on unlabelled data to identify patterns or relationships in the data.
Reinforcement learning: Training a model to make decisions based on feedback received from the environment.
Fill in the blanks:
___________ is a subdomain of artificial intelligence (AI) that involves the use of statistical and
specialized algorithms to enable computer systems to learn and improve from experience without
being explicitly programmed.
10.1.2 Natural Language Processing
Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on enabling machines to
understand, interpret, and generate human language. NLP involves the use of computational algorithms to analyze
and manipulate natural language data, such as text, speech, and video.
Emerging Trends 299

