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Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
EnglishSelf Study, Virtual ClassroomVideo and Text Based

Courses and Certificate Fees

Certificate AvailabilityCertificate Providing Authority
yesHaas School of Business, Berkeley

The Syllabus

  • Module 1: Introduction to Machine Learning
  • Module 2: Fundamentals of Machine Learning
  • Module 3: Introduction to Data Analysis
  • Module 4: Fundamentals of Data Analysis
  • Module 5: Practical Applications I

  • Module 6: Clustering and Principal Component Analysis
  • Module 7: Linear and Multiple Regression
  • Module 8: Feature Engineering and Overfitting
  • Module 9: Model Selection and Regularization
  • Module 10: Time Series Analysis and Forecasting
  • Module 11: Practical Applications II
  • Module 12: Classification and k-Nearest Neighbors
  • Module 13: Logistic Regression
  • Module 14: Decision Trees
  • Module 15: Gradient Descent and Optimization
  • Module 16: Support Vector Machines
  • Module 17: Practical Applications III

  • Module 18: Natural Language Processing
  • Module 19: Recommendation Systems
  • Module 20: Capstone I
  • Module 21: Ensemble Techniques (GBM, XGB, and Random Forest)
  • Module 22: Deep Neural Networks I
  • Module 23: Deep Neural Networks II
  • Module 24: Capstone II

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