Professional Certificate in Machine Learning and Artificial Intelligence

Professional Certificate in Machine Learning and Artificial Intelligence

Launch Your Career in ML/AI

Click here for informational brochure.

Program Faculty: Gabriel Gomes,  Joshua Hug, Reed Walker, Jonathan Kolstad

Program Overview

Technologies driven by machine learning (ML) and artificial intelligence (AI) have transformed industries and everyday life — from facial and voice recognition software to intelligent robotics for manufacturing, life-saving medical diagnostics, self-driving vehicles, and much more. The possibilities for ML/AI applications are virtually unlimited and sought after in practically every industry segment. That’s why global organizations are actively recruiting IT professionals with the specialized skills and proficiencies needed to develop future ML/AI technological innovations.

It’s an ideal time to launch your career in ML/AI engineering. Grandview Research reports that the global AI market is valued at $62.35 billion, of which North America accounted for over a 40% share of revenue in 2020. Furthermore, the global AI market is predicted to expand at a compound annual growth rate of 40.2% from 2021 to 2028.

However, if you want a rewarding career in this high-paying IT segment, you will need to demonstrate the ability to solve complex problems with today’s ML/AI tools.

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. Over the course of this program, you will gain hands-on experience solving real-world technical and business challenges using the latest ML/AI tools available. You will leave the program with highly sought-after skills that will help you build a successful career in this field.

Who is this Program Designed For?

This program is designed to provide learners with the fundamental 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 and 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

Future Job Titles

This program will equip you with the hands-on skills needed to launch or accelerate your career in ML and AI. Representative job titles include:

  • Data Scientist
  • Machine Learning Scientist
  • Machine Learning Engineer
  • Artificial Intelligence Engineer

Program Topics

The 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 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: Pratical 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 to 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 this 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