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Course Report - Bayesian Thinking

Course Report

Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.

Course Features

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Duration

3 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

3 hours

Course Description

Bayesian methods offer a different way of thinking about probability. They have applications in business decision making.

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Highlights

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Pedagogy

Top 20 Percentile

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Course Credibility

Top 20 Percentile

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Parameters

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Course Credibility

Delivered through Corporate Finance Institute (CFI) a renowned institution in the field, this course offers a comprehensive learning experience.

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Pedagogy

This comprehensive course equips you with all major Statistics & Probability skills applicable to your daily life. Personalized teaching ensures one-on-one doubt resolution with faculty, maximizing skill acquisition. These practical skills empower you to confidently apply your knowledge and thrive in various real-life situations. An exceptional course in Statistics & Probability, this stands out for its Self Paced learning approach. Learners have the flexibility to progress at their own speed, tailoring the experience to their individual needs.

Course Overview

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Personlized Teaching

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Case Based Learning

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Post Course Interactions

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Case Studies,Instructor-Moderated Discussions

Skills You Will Gain

What You Will Learn

Describe, compare, and contrast the three main approaches to probability

Understand the fundamentals of the Bayesian approach—such as conditional probability, priors, and updating beliefs

Apply Bayesian methods such as Bayes theorem and contingency tables to simple problems

Describe two Bayesian machine learning methods—multinomial and gaussian Bayes classifiers

Recognize the benefits of using these machine learning methods for modeling complex scenarios

Evaluate the results of the machine learning tests against business goals in Python

Target Students

Business Intelligence Analyst

Data Scientist

Data Visualization Specialist

Course Cover
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