What you'll get
  • 2+ Hours
  • 2 Courses
  • Course Completion Certificates
  • Self-paced Courses
  • Technical Support
  • Case Studies

Synopsis

  • Covers advanced techniques for exploring, preparing, and cleaning complex datasets to ensure high-quality data.
  • Introduces Bayesian table analysis methods to uncover hidden patterns, correlations, and insights.
  • Guides learners in implementing Bayesian machine learning models using Markov Chain Monte Carlo (MCMC) techniques.
  • Teaches multiple variant-testing methodologies, including A/B testing and adaptive algorithms, for data-driven decision-making.
  • Provides practical, hands-on experience solving case studies and statistical problems in Excel and Python.
  • Explains the Naive Bayes classifier and its applications in machine learning.
  • Offers strategies for interpreting Bayesian model outputs and applying findings to real-world projects or research.
  • Focuses on evaluating model performance and validating results for reliable outcomes.
  • Emphasizes the application of Bayesian principles and advanced modeling techniques to extract actionable insights across domains.

Content

Courses No. of Hours Certificates Details
Bayesian Machine Learning: A/B Testing57m✔View Curriculum
Project on Bayesian Statistics: Bayesian Model for Healthcare Testing1h 44m✔View Curriculum

Description

This course introduces participants to Bayesian statistics, a framework that applies probability to statistical problems and updates beliefs about random events with new data. Bayes' theorem uses prior knowledge and new data to calculate posterior probabilities, aiding in prediction and decision-making. Participants also explore machine learning techniques, focusing on algorithms that learn from data to make predictions or informed decisions, closely linking them to computational statistics.

Course Overview:

  • Introduction: Participants are introduced to the course objectives and to the significance and potential impact of Bayesian and machine learning methods.
  • Data Exploration and Preparation: Learners engage in hands-on data exploration, preprocessing, and cleaning, gaining skills to manage datasets effectively for in-depth analysis.
  • Bayesian Table Analysis: Participants apply Bayesian techniques to uncover patterns, correlations, and insights from datasets through practical examples and case studies.
  • Bayesian Machine Learning & Multiple Variant Testing: Advanced topics include Bayesian modeling, Markov Chain Monte Carlo (MCMC) simulations, and multiple-variant testing to inform decisions from experimental data.

The course emphasizes practical applications, preparing participants to implement Bayesian and machine-learning methods in real-world settings. By the end of the course, learners can leverage these techniques for data-driven decision-making and achieve impactful outcomes in professional or research projects.

Requirements

  • Prior exposure to machine learning.
  • Basic knowledge of Python programming.
  • Understanding of statistics.

Target Audience

  • Individuals interested in data and analytics.
  • Data engineers, Architects, Analysts, Software Engineers, IT Operations, and Technical managers.