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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.
Eduonix Learning Solutions
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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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
Eduonix Learning Solutions
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.
Eduonix Learning Solutions
• 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
Eduonix Learning Solutions
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
Eduonix Learning Solutions
Approaches
Eduonix Learning Solutions
Employment Hike
Eduonix Learning Solutions
Salary In Demand
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!
Eduonix Learning Solutions
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!
Eduonix Learning Solutions
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
Eduonix Learning Solutions
Actual Course Price - $ 49
Offer Price - $ 10
-----------------------------
Total Savings - $ 39
Eduonix Learning Solutions
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http://bit.ly/2nL8TRu | @Eduonix
http://bit.ly/2ng0DVR |@Tutor_Eduonix
Be update on
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Enroll For $10 & Get Started With Machine Learning Now!
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Eduonix Learning Solutions
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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. Eduonix Learning Solutions
  • 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 Eduonix Learning Solutions
  • 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 Eduonix Learning Solutions
  • 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. Eduonix Learning Solutions
  • 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 Eduonix Learning Solutions
  • 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 Eduonix Learning Solutions
  • 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! Eduonix Learning Solutions
  • 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! Eduonix Learning Solutions
  • 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 Eduonix Learning Solutions
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