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What is Machine Learning ?
“Machine Learning is the science of getting computers
to learn and act like humans do, and improve their
learning over time in autonomous fashion, by feeding
them data and information in the form of observations
and real-world interactions.”
According to Stanford & University of Washington
“Machine learning is the science of getting computers to
act without being explicitly programmed.” – Stanford
“Machine learning algorithms can figure out how to
perform important tasks by generalizing from
examples.” – University of Washington
Basic Concepts of Machine Learning
Did you know?
In machine learning, a target is called a label.
In statistics, a target is called a dependent variable.
A variable in statistics is called a feature in machine learning.
A transformation in statistics is called feature creation in
machine learning.
What's required to create good machine learning systems?
Data preparation capabilities.
Algorithms – basic and advanced.
Automation and iterative processes.
Scalability.
Ensemble modeling.
What does Machine learning do?
It enables the computers or the machines to
make data-driven decisions rather than being
explicitly programmed for carrying out a certain
task. These programs or algorithms are designed
in a way that they learn and improve over time
when are exposed to new data.
Image source : edurekaHow does Machine Learning Work?
There are many different types of machine learning algorithms,
with hundreds published each day, and they’re typically grouped
by either learning style (i.e. supervised learning, unsupervised
learning, semi-supervised learning) or by similarity in form or
function (i.e. classification, regression, decision tree, clustering,
deep learning, etc.).
Regardless of learning style or function, all combinations
of machine learning algorithms consist of the following:
Representation (a set of classifiers or the language that a computer
understands)
Evaluation (aka objective/scoring function)
Optimization (search method; often the highest-scoring classifier, for
example; there are both off-the-shelf and custom optimization methods
used)
Image credit: Dr. Pedro Domingo, University of Washington
Visual Representations of Machine Learning Models
Decision tree model Gaussian mixture model
Dropout neural network
Merging chrominance and luminance using Convolutional
Neural Networks

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Machine learning

  • 1. What is Machine Learning ?
  • 2. “Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”
  • 3. According to Stanford & University of Washington “Machine learning is the science of getting computers to act without being explicitly programmed.” – Stanford “Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.” – University of Washington
  • 4. Basic Concepts of Machine Learning
  • 5. Did you know? In machine learning, a target is called a label. In statistics, a target is called a dependent variable. A variable in statistics is called a feature in machine learning. A transformation in statistics is called feature creation in machine learning.
  • 6. What's required to create good machine learning systems? Data preparation capabilities. Algorithms – basic and advanced. Automation and iterative processes. Scalability. Ensemble modeling.
  • 7. What does Machine learning do? It enables the computers or the machines to make data-driven decisions rather than being explicitly programmed for carrying out a certain task. These programs or algorithms are designed in a way that they learn and improve over time when are exposed to new data.
  • 8. Image source : edurekaHow does Machine Learning Work?
  • 9. There are many different types of machine learning algorithms, with hundreds published each day, and they’re typically grouped by either learning style (i.e. supervised learning, unsupervised learning, semi-supervised learning) or by similarity in form or function (i.e. classification, regression, decision tree, clustering, deep learning, etc.).
  • 10. Regardless of learning style or function, all combinations of machine learning algorithms consist of the following: Representation (a set of classifiers or the language that a computer understands) Evaluation (aka objective/scoring function) Optimization (search method; often the highest-scoring classifier, for example; there are both off-the-shelf and custom optimization methods used)
  • 11. Image credit: Dr. Pedro Domingo, University of Washington
  • 12. Visual Representations of Machine Learning Models Decision tree model Gaussian mixture model
  • 13. Dropout neural network Merging chrominance and luminance using Convolutional Neural Networks