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2. What Is Machine Learning?
Machine learning is a field of computer
science that gives computer systems the
ability to "learn" (i.e., progressively improve
performance on a specific task) with data,
without being explicitly programmed.
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3. The name machine learning was coined in 1959
by Arthur Samuel. Evolved from the study of pattern
recognition and computational learning
theory in artificial intelligence, machine learning
explores the study and construction of algorithms
that can learn from and make predictions on data –
such algorithms overcome following strictly
static program instructions by making data-driven
predictions or decisions, through building
a model from sample inputs.
Machine Learning History
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4. Machine Learning Methods
Machine learning tasks are typically classified into two broad
categories, depending on whether there is a learning "signal"
or "feedback" available to a learning system:
 Supervised learning
o Semi-supervised learning
o Active learning
o Reinforcement learning
 Unsupervised learning
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5. Machine Learning Applications
Another categorization of machine learning tasks arises when one considers the desired output of
a machine-learned system:
 In Classification, inputs are divided into two or more classes, and the learner must produce a
model that assigns unseen inputs to one or more (multi-label classification) of these classes.
 In Regression, also a supervised problem, the outputs are continuous rather than discrete.
 In Clustering, a set of inputs is to be divided into groups.
 Density estimation finds the distribution of inputs in some space.
 Dimensionality reduction simplifies inputs by mapping them into a lower-dimensional space.
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6. • Bayesian networks
• Reinforcement learning
• Representation learning
• Similarity and metric learning
• Sparse dictionary learning
• Genetic algorithms
Approaches
• Decision tree learning
• Association rule learning
• Artificial neural networks
• Inductive logic programming
• Support vector machines
• Clustering
• Rule-based machine learning
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7. Applications • Computer vision, including object recognition
• Detecting credit-card fraud
• General game playing
• Information retrieval
• Internet fraud detection
• Linguistics
• Marketing
• Machine learning control
• Machine perception
• Medical diagnosis
•Automated theorem proving
•Adaptive websites
•Affective computing
•Bioinformatics
•Brain–machine interfaces
•Cheminformatics
•Classifying DNA sequences
•Computational anatomy
•Computer Networks
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8. Approaches
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10. Course Description
A complete comprehensive course that covers a variety of different machine learning concepts such
as supervised learning, unsupervised learning, reinforced learning and even neural networks. But
that’s not all. In addition to understanding the theory behind machine learning, you will then
actually use these concepts and implement them into actual projects to see how they work in action!
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11. Explore the following in detail
• Breakdown of important concepts required in machine learning
• Detailed analysis of the different types of machine learning
• How to integrate the algorithms in actual Python Projects
• Different types of machine learning
• Quizzes to help evaluate your learning
• What is machine learning
• And much more!
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12. Course Sections
 Section 1 : An Introduction to Machine Learning
 Section 2 : Supervised Learning - Part 1
 Section 3 : Unsupervised Learning
 Section 4 : Neural Networks
 Section 5 : Real World Machine Learning
 Section 6 : Final Project
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13. Actual Course Price - $ 49
Offer Price - $ 10
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Total Savings - $ 39
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