An Intuitive Intro to Probability
Gain essential skills in Probability Theory for managing uncertainty, structured into five modules with practical exercises, covering topics like Probability, Conditional Probability, and offering an engaging online learning experience.
Description for An Intuitive Intro to Probability
Features of Course
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by University of Zurich
Duration: 3 weeks at 9 hours a week
Schedule: Flexible
Pricing for An Intuitive Intro to Probability
Use Cases for An Intuitive Intro to Probability
FAQs for An Intuitive Intro to Probability
Reviews for An Intuitive Intro to Probability
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for An Intuitive Intro to Probability
Investigate the field of artificial intelligence and machine learning. While investigating the transformative disciplines of artificial intelligence, machine learning, and deep learning, enhance your Python abilities.
Master the AI and machine learning toolkit. Mathematics for Machine Learning and Data Science is a Specialization that is accessible to beginners. In this program, you will acquire the basic mathematics tools of machine learning, including calculus, linear algebra, statistics, and probability.
Understand and apply statistical techniques to quantify prediction uncertainty, analyze probability distributions, and evaluate machine learning model efficacy using interval estimates and margins of error.
Featured Tools
Reinforce Your Career: The Role of Machine Learning in Finance. Enhance your understanding of the algorithms and instruments required to forecast financial markets.
Learn to build a machine learning pipeline using DataIku's AutoML feature to forecast COVID-19 fatalities with over 90% accuracy without coding.
A comprehensive course on machine learning using Python, covering deep learning, GANs, image processing, various algorithms, and industrial applications, accessible to all skill levels.
Create a final presentation to evaluate peer projects, train neural networks for regression and classification, and develop Python-based recommender systems. Additionally, employ KNN, PCA, and collaborative filtering.
The course investigates the integration of AI with medical practice, science, and commerce, as well as the ways in which machine learning addresses healthcare challenges and impacts patient care quality and safety.