Professional Certificate in Machine Learning and Artificial Intelligence
Emeritus Institute of Management
Key Information
Campus location
Online
Languages
English
Study format
Distance Learning
Duration
6 months
Pace
Full time, Part time
Tuition fees
USD 7,500
Application deadline
Request info
Earliest start date
Request info
Scholarships
Explore scholarship opportunities to help fund your studies
Introduction
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)
Ideal Students
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
Program Outcome
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 Experience
- Learn from UC Berkeley's globally recognized faculty
- Earn a certificate of completion from UC Berkeley Executive Education
- Learn how to implement the ML/data science lifecycle within your own organization
- 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.
Curriculum
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
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.
Career Opportunities
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