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Principles of Machine Learning

With the help of this course of data science, you will be provided complete details of the theory of machine learning joined hands-on experience and practical scenarios validating, building, and deploying the models of machine learning.


Self-Paced

Learning Style

Microsoft

Provider

Intermediate

Difficulty

24 Hours

Course Duration

Course Info

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With the help of this course of data science, you will be provided complete details of the theory of machine learning joined hands-on experience and practical scenarios validating, building, and deploying the models of machine learning.

Course Information

About this course:

To run predictive models, machine learning uses systems that gain from existing information so as to estimate future outcomes, behaviors, and trends.

With the help of this course of data science, you will be provided complete details of the theory of machine learning joined hands-on experience and practical scenarios validating, building, and deploying the models of machine learning. You will figure out how to create and get experiences from these models utilizing R, and Azure Notebooks.

Course Objective:

  • Explore classification
  • Building Classification Models
  • Regression in machine learning
  • Creating Regression Models
  • Explain the progress of supervised models
  • Techniques for Improving Models
  • Details on non-linear modeling
  • Clustering
  • Introduction to Decision Trees
  • Recommender systems
  • This course hands-on elements leverage an arrangement of Python, R, and Machine Learning of MS Azure.

Audience:

Machine Learning Engineer

AI developer

Prerequisite:

No prerequisite required for this course.

Outline

More Information

More Information
BrandMicrosoft
SubjectsCloud Computing, Virtualization
Lab AccessNo
TechnologyMicrosoft
Learning StyleSelf-Paced Learning
Learning TypeCourse
DifficultyIntermediate
Course Duration24 Hours
LanguageEnglish
VPA DiscountVPA Discount

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