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Machine learning algorithms can learn from different kinds of information, such as pictures, text, sensor readings,
and past data, by figuring out patterns in the data to guess or decide things. Additionally, some common machine
learning methods such as decision trees, neural networks, and support vector machines enable this learning process.
Features of Machine Learning
Some key features of machine learning are as follows:
● ML interprets, analyses, and processes data to address real-world problems.
● It learns from data and enhances its performance over time.
● The technology facilitates automation and prediction based on learned patterns.
● It is the prevailing approach in contemporary AI.
● It employs data analysis, training, and sometimes human review to refine its capabilities.
● Unlike traditional programming, it doesn’t rely on predefined rules but learns from examples and experiences.
● It powers a wide range of applications across industries, from healthcare and finance to autonomous vehicles and
recommendation systems.
However, ML is not without its challenges. Overfitting, in which models specialise too much on training data, can
result in poor performance on fresh data. Bias in training data can lead to skewed predictions, and some models
are difficult to understand, serving as black boxes. Despite these hurdles, machine learning (ML) converts data into
knowledge, allowing computers to learn, adapt, and make autonomous judgments.
Types of Machine Learning
Machine learning can be divided into three primary categories, each distinguished by its learning approach and nature of
the input data:
• Labelled data
• Direct feedback
• Predict outcome
Supervised
Learning
• No labels
Machine Unsupervised • No feedback
Learning Learning • Find hidden structure
Reinforcement
Learning
• Decision process
• Reward system
• Learn series of actions
Supervised Learning
Supervised learning is a type of machine learning in which machines are trained using well "labelled" training data,
and on basis of that data, machines predict the output. The labelled data means some input data is already tagged
with the correct output.
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