Machine Learning
                     MACHINE 
LEARNING
What is machine 
learning?
Machine learning (ML) is a type of artificial intelligence 
(AI) that allows software applications to become more 
accurate at predicting outcomes without being explicitly 
programmed to do so. 
Machine learning algorithms use historical data as input 
to predict new output values.
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Why is machine 
learning 
important?
Machine learning is important because it gives 
enterprises a view of trends in customer behavior and 
business operational patterns, as well as supports the 
development of new products. 
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Different types of 
machine learning
There are four basic approaches: supervised learning, 
unsupervised learning, semi-supervised learning and 
reinforcement learning. The type of algorithm data 
scientists choose to use depends on what type of data 
they want to predict.
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How does supervised 
machine learning 
work?
Supervised machine learning requires the data scientist 
to train the algorithm with both labeled inputs and 
desired outputs.
Supervised learning algorithms are good for the following 
tasks:
• Binary classification: Dividing data into two categories.
• Multi-class classification: Choosing between more than two types of 
answers.
• Regression modeling: Predicting continuous values.
• Ensembling: Combining the predictions of multiple machine learning 
models to produce an accurate prediction.
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How does 
unsupervised 
machine learning 
work?
Unsupervised machine learning algorithms do not require 
data to be labeled. They sift through unlabeled data to 
look for patterns that can be used to group data points 
into subsets. 
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How does semi-
supervised learning 
work?
Semi-supervised learning works by data scientists feeding 
a small amount of labeled training data to an algorithm. 
From this, the algorithm learns the dimensions of the 
data set, which it can then apply to new, unlabeled data.
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How does 
reinforcement learning 
work?
Reinforcement learning works by programming an 
algorithm with a distinct goal and a prescribed set of 
rules for accomplishing that goal. 
Reinforcement learning is often used in areas such as:
• Robotics: Robots can learn to perform tasks the physical world using this 
technique.
• Video gameplay: Reinforcement learning has been used to teach bots to 
play a number of video games.
• Resource management: Given finite resources and a defined goal, 
reinforcement learning can help enterprises plan out how to allocate 
resources.
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What is the future of 
machine learning?
While machine learning algorithms have been around for 
decades, they've attained new popularity as artificial 
intelligence has grown in prominence. Deep learning 
models, in particular, power today's most advanced AI 
applications.
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Machine learning 
platforms
Machine learning platforms are among enterprise 
technology's most competitive realms, with most major 
vendors, including Amazon, Google, Microsoft, IBM and 
others, racing to sign customers up for platform services 
that cover the spectrum of machine learning activities, 
including data collection, data preparation, data 
classification, model building, training and application 
deployment.
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THANK 
YOU
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