Data Analytics Foundations for Accountancy II

Instructor: Robert J. Brunner

Skills you'll gain

  •   Decision Tree Learning
  •   Classification And Regression Tree (CART)
  •   Regression Analysis
  •   Machine Learning Algorithms
  •   Predictive Analytics
  •   Feature Engineering
  •   Statistical Machine Learning
  •   Scikit Learn (Machine Learning Library)
  •   Machine Learning
  •   Applied Machine Learning
  •   Anomaly Detection
  •   Data Processing
  •   Data Ethics
  •   Unsupervised Learning
  •   Supervised Learning
  • There are 9 modules in this course

    To begin, I recommend taking a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!

    Module 1: Introduction to Machine Learning

    Module 2: Fundamental Algorithms

    Module 3: Practical Concepts in Machine Learning

    Module 4: Overfitting & Regularization

    Module 5: Fundamental Probabilistic Algorithms

    Module 6: Feature Engineering

    Module 7: Introduction to Clustering

    Module 8: Introduction to Anomaly Detection

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