Data Analytics Foundations for Accountancy II

Instructor: Robert J. Brunner

Skills you'll gain

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