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Promotion and Events Specialist

Professional Certificate in Machine Learning and Artificial Intelligence

Gain cutting-edge ML/AI skills and accelerate your career in this 6-month online program.

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Course Date

STARTS ON

February 28, 2023

Course Duration

DURATION

6 months, online
15–20 hours per week

Course Fee

PROGRAM FEE

US$7,500 US$6,900 or get US$750 off with a referral

Course Information Flexible payment available
Course Fee

For Your Team

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Application Details

program fee

US$7,500 US$6,900

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Launch Your Career in ML/AI

Machine learning (ML) and artificial intelligence (AI) are transforming the way organizations do business and how consumers live. That's why IT professionals with the specialized knowledge and skills to develop the next generation of ML/AI technology innovations are in immediate demand globally and across industries.

So how can you kick-start your career in this exciting, in-demand field? The Professional Certificate in Machine Learning and Artificial Intelligence from UC Berkeley (ranked the #1 university in the world by Forbes magazine) is built in collaboration with the College of Engineering and the Haas School of Business.

In six months, you will gain foundational as well as advanced knowledge of ML/AI along with insights into the business applications of these technologies from UC Berkeley's world-class faculty. You will gain practical, hands-on experience using cutting-edge ML/AI tools in addition to career guidance to succeed in this fast-paced field. Take the next step in your career by gaining market-ready ML/AI skills with this professional certificate program.

$120,844

The average salary for an AI/ML engineer in the US in 2022

Source: Glassdoor

97 million

The estimated number of new AI-related jobs between 2022 and 2025

Source: Forbes

$15.7 trillion

AI's projected contribution to the global economy by 2030

Source: Forbes
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Who Is This Program For?

This program is designed to provide learners with the essential knowledge and practical applications of ML/AI tools and frameworks needed to transition into an exciting, high-demand career in this field. This program is for anyone with a technology or math background, including:

  • IT and engineering professionals who want to unlock new opportunities for career growth or chart a cutting-edge career path
  • Data and business analysts who want to gain better growth trajectories
  • Recent science, technology, engineering, and mathematics (STEM) graduates and academics who want to enter the private sector and scale the positive impact of evolving technologies

Applicants must have:

  • A bachelor's degree or higher
  • Strong math skills
  • Some programming experience

Also recommended:

  • An educational background in STEM fields
  • Technical work experience
  • Some experience with Python,R, or SQL
  • Some experience with statistics and calculus

Key Takeaways

  • Develop a comprehensive understanding of ML/AI concepts and identify the best ML models to fit various business situations.
  • Learn how to implement the ML/data science life cycle and devise cutting-edge solutions to real-life problems within your own organization.
  • Interact and collaborate with industry experts to understand the technical and business applications of ML/AI.
  • Develop a market-ready GitHub portfolio to show prospective employers.
  • Learn from UC Berkeley's globally recognized faculty and gain a verified digital certificate of completion from UC Berkeley Executive Education.

Program Topics

This program is organized into three main sections:

Section 1: Foundations of ML/AI
Your learning journey will commence with exploring the basic concepts, and industry-standard notations in ML/AI and exploring the real-world contexts for the data science lifecycle. It then progresses to drawing business conclusions from data sets and visualizations.

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

Section 2: ML/AI Techniques
In this section, you will gain hands-on experience with coding in Python to create k-means algorithms and apply functions. You will also learn how to predict outcomes using multiple linear regression models, create visual decision trees, and interpret various kinds of ML/AI decision models.

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

Section 3: Advanced Topics and Capstone
In the final section, you will gain a deeper understanding of advanced ML/AI concepts, such as Natural Language Processing and Deep Neural Networks. You will also conduct research and analysis to complete your capstone project in ML/AI.

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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Capstone Project

The knowledge gained each week in this ML/AI program prepares you to conduct your own research and analysis in a capstone project. You will gain the opportunity to interact with industry experts to identify a specific problem within your field and leverage their expertise along with the concepts, models, and tools taught in the program to devise a solution to your chosen problem. By the end of the program, you will come away with a professional-quality GitHub portfolio presentation that you can share on your LinkedIn profile or with potential employers.

Program Experience

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Learn from UC Berkeley's globally recognized faculty

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Earn a certificate of completion from UC Berkeley Executive Education

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Learn how to implement the ML/data science lifecycle within your own organization

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Build a GitHub portfolio to share with recruiters and potential employers

Tools and Resources in the Program

Over the course of this program, you will gain hands-on coding experience with Python, Jupyter, pandas, Seaborn, Plotly, and GitHub.

Decorative image depicting the tool and platforms used
Decorative image depicting the tool and platforms used

Program Faculty from Berkeley Engineering

Faculty Member Gabriel Gomes

Gabriel Gomes

Researcher and lecturer with the Mechanical Engineering Department and the Institute of Transportation Studies at UC Berkeley

Gabriel Gomes is a researcher and lecturer with the Mechanical Engineering Department and the Institute of Transportation Studies at UC Berkeley. He received a doctorate degree in automatic control theory in 2004 from UC Berkeley... More info

Faculty Member Joshua Hug

Joshua Hug

Associate Teaching Professor with the department of Electrical Engineering and Computer Sciences at UC Berkeley

Joshua Hug has been with the department of Electrical Engineering and Computer Sciences at UC Berkeley since 2014 and was a lecturer at Princeton University from 2011 to 2014. He received his Ph.D. in 2011 from UC Berkeley, with research focused on computational models of bacterial signal processing and decision making... More info

Faculty Member Gabriel Gomes

Gabriel Gomes

Researcher and lecturer with the Mechanical Engineering Department and the Institute of Transportation Studies at UC Berkeley

Gabriel Gomes is a researcher and lecturer with the Mechanical Engineering Department and the Institute of Transportation Studies at UC Berkeley. He received a doctorate degree in automatic control theory in 2004 from UC Berkeley. Since then, he has focused his research on various problems in the modeling, simulation, and control of traffic networks. As a lecturer at UC Berkeley, he has taught courses in partial differential equations, control theory, and mathematical modeling. He also supervises capstone projects with the Master of Engineering program of the Fung Institute. These projects cover a wide range of topics, including robotics, solar energy, machine learning, natural language processing, traffic simulation, reinforcement learning, autonomous vehicles, and smart exercise machines. He is the author of over 50 papers in various areas of engineering.
Faculty Member Joshua Hug

Joshua Hug

Associate Teaching Professor with the department of Electrical Engineering and Computer Sciences at UC Berkeley

Joshua Hug has been with the department of Electrical Engineering and Computer Sciences at UC Berkeley since 2014 and was a lecturer at Princeton University from 2011 to 2014. He received his Ph.D. in 2011 from UC Berkeley, with research focused on computational models of bacterial signal processing and decision making. He received his B.S. in electrical engineering in 2003 from the University of Texas at Austin. In 2017, he received the Diane S. McEntyre Award for Excellence in Teaching Computer Science, and in 2018, he received the Jim and Donna Gray Award for Excellence in Undergraduate Teaching of Computer Science. He has taught courses in artificial intelligence, data structures, rule-based and generative art, information security, data science, and the social implications of computing.

Business Experts from Berkeley Haas

Faculty Member Reed Walker

Reed Walker

Associate Professor of Business and Public Policy and Economics at UC Berkeley

Reed Walker is an associate professor of business and public policy and economics at UC Berkeley. His research explores the social costs of environmental externalities, such as air pollution, and how regulations to limit these externalities contribute to gains and/or losses to the economy. He is the faculty codirector of the UC Berkeley Opportunity Lab’s Climate and Environment Initiative. He is also a research associate at the Energy Institute at Berkeley, a faculty research fellow at the National Bureau of Economic Research, and a research fellow at IZA. He received his Ph.D. in economics from Columbia University.

Faculty Member Jonathan Kolstad

Jonathan Kolstad

Associate Professor | Egon & Joan Von Kaschnitz Distinguished Professorship

Jonathan Kolstad is an Associate Professor of Economic Analysis and Policy at Berkeley Haas and a research associate at the National Bureau of Economic Research. He is also the codirector of the Health Initiative at the UC Berkeley Opportunity Lab. He is an economist whose research interests lie at the intersection of health economics, industrial organization, and public economics. He is particularly interested in finding new models and unique data that can account for the complexity of markets in health care, notably the role of information asymmetries and incentives. He is also a cofounder and was chief data scientist at Picwell. He received his Ph.D. from Harvard University and B.A. from Stanford University.

Career Preparation and Guidance

Transitioning to a career in ML/AI engineering requires a variety of hard and soft skills. This program guides you as you navigate your journey to your new career path, including crafting an elevator pitch and communication tips. These services are provided by Emeritus, our learning collaborator for this program. The program support team includes program facilitators who will help you reach your learning goals and career coaches to guide you through your job search. Our primary goal is to give you the skills needed to be prepared for a job in this field; however, job placement is not guaranteed.

Emeritus provides the following career preparation services:

  • Crafting your elevator pitch
  • Navigating your job search
  • LinkedIn profile guidance
  • Interview tips and preparation
  • Resume/cover letters
  • Negotiating salary

Career exercises focused on launching a career in ML/AI:

  • Job search and interviewing for ML/AI positions
  • Communicating ML/AI concepts through presentation skills
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Certificate

Example image of certificate that will be awarded after successful completion of this program

Certificate

Get recognized! Upon successful completion of the program, UC Berkeley Executive Education grants a verified digital certificate of completion to participants. Participants must complete 80% of the required activities including a capstone project (if any) to obtain the certificate of completion. This program also counts toward a Certificate of Business Excellence.

Successful completion of this program fulfills four curriculum days (minimum requirement of 17 curriculum days) towards the UC Berkeley Certificate of Business Excellence (COBE).
Learn more on how it works here.

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Note: This program results in a digital certificate of completion and is not eligible for degree credit/CEUs. After successful completion of the program, your verified digital certificate will be emailed to you in the name you used when registering for the program. All certificate images are for illustrative purposes only and may be subject to change at the discretion of UC Berkeley College of Engineering, Haas School of Business, and Berkeley Executive Education.

Registration for this program is done through Emeritus. You can contact us at berkeley@emeritus.org or schedule a call with an advisor.

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Early registrations are encouraged. Seats fill up quickly!

Flexible payment options available. Learn more.