Professional Certificate in Machine Learning and Artificial Intelligence

Module 1: Introduction to Machine Learning

Module 2: Fundamentals of Statistics and Distribution Functions

Module 3: Introduction to Data Analytics

Module 4: Fundamentals of Data Analytics

Module 5: Practical Applications I

Module 6: Data Clustering and Principal Component Analysis (PCA)

Module 7: Linear and Multiple Regressions

Module 8: Feature Engineering and Overfitting

Module 9: Model Selection and Regularization

Module 10: Time Series Analysis and Forecasting

Module 11: Practical Application II

Module 12: Classification and K-Nearest Neighbors

Module 13: Logistic Regression

Module 14: Decision Trees

Module 15: Gradient Descent and Optimization

Module 16: Classifying Nonlinear Features

Module 17: Practical Application III

Module 18: Natural Language Processing

Module 19: Recommendation Systems

Module 20: Ensemble Techniques

Module 21: Deep Neural Networks I

Module 22: Deep Neural Networks II

Module 23: Introduction to Generative AI

Module 24: Capstone Project

Join local alumni chapters or clubs in your region

Participate in the annual Berkeley Haas Alumni Conference

Attend select Berkeley Haas and Berkeley Executive Education Networking events open to the COBE community

Activate an @haas.executivealumni.berkeley.edu email forwarding address

15% discount on the list price of future eligible Berkeley Executive Education programs.

30% discount on the list price of future eligible programs after completion of your Certificate of Business Excellence.

Public visitor access to select campus libraries and university database services

A one-year complimentary digital subscription to California Management Review

Berkeley Haas Alumni newsletter

Berkeley Haas Alumni Jobs e-Newsletter featuring job postings from distinguished employers

Haas Insights offering the latest research and thought leadership from industry speakers and faculty

A bachelor's degree or higher

Strong mathematical skills

Some programming experience

An educational background in STEM fields

Technical work experience

Some experience with Python,R, or SQL

Some experience with statistics and calculus

ML/AI foundations: Develop a comprehensive understanding of ML/AI concepts, and identify the best ML models to fit various business situations.

ML life cycle: Learn how to implement the ML/data science life cycle, and devise cutting-edge solutions to real-life problems within your organization.

Industry applications: Interact and collaborate with industry experts to understand the technical and business applications of ML/AI.

Generative AI analysis: Analyze generative AI models, including ChatGPT, and test their efficacy.

Generative AI use cases: Explore innovative business applications for generative AI technologies.

GitHub portfolio: Develop a market-ready GitHub portfolio to showcase your ML/AI skills and project work to prospective employers.