AI & Machine Learning Engineering Certificate Bootcamp
- Hands-on Practice
- Project Based Learning
- No Experience Necessary
- Complete in as little as 15 weeks
- Reasonably Priced Tuition Rates with Payment Plan Options
Our curriculum evolves with current industry practices.
- Structured for all learning types
- Flexible schedule
- Instructor feedback and support
- Experienced instructors with a passion for learning
- Scheduled office hours for student needs
- Supportive and responsive to student needs
Our flex program is designed to give students the opportunity to attend live interactive lectures, study coursework, and complete assignments throughout the week. The program consists of six, five-week courses.
- Interactive Lectures hosted and recorded weekly
- Individual Lab Work / Projects
- Homework Assignments
- Credit-Bearing Courses
Benefit for Students & Alumni
A career path in tech requires a lifetime commitment to learning new skills and technologies. We hold periodic elective workshops for students and alumni to help provide continuing education opportunities in a myriad of complementary topics. Every technology professional has heard the interview question, "Do you have experience with _?" This is our way of filling in the blank.
Students receive career support with their program which includes portfolio guidance, mock interviews, and virtual career fairs. In addition, our dedicated Student Services Team is available to provide detailed feedback on everything from cover letters and resumes, to projects, portfolios, and emails to potential employers.
What is Machine Learning?
Machine Learning (ML) is a component of Artificial Intelligence (AI) that is focused on creating systems that imitate the way humans learn from large amounts of data. ML involves computer programming, but not in the traditional way. In traditional programming, we write code for computers to perform specific tasks, step-by-step. In ML, we use algorithms that allow the computer to learn patterns and make decisions based on data. The process involves feeding large amounts of data into a machine learning model and training the model so it can learn to recognize patterns. After it is trained, the model can make predictions, or even decisions, based on new data.
Why is Machine Learning Important?
Machine learning’s overall importance is centered on its ability to process and learn from data in ways that humans cannot. This can lead to significant advancements, tremendous efficiencies, and innovations across a wide spectrum of industries. Learning machine learning programming can be important because, although the model does the learning, we still need to write code to create and manage the models, preprocess data, and analyze the results.
This program is designed to help students with basic to intermediate software development skills learn how to design and develop artificial intelligence (AI) systems. Architectural frameworks, development tools, modern programming languages, and best practices are explored. This hands-on program gives students opportunities to put theory into practice. By the end of this program students should be able to architect an AI system and implement AI functionality through programming and service connections.
Artificial Intelligence Fundamentals Certificate Program
Intro to Programming Fundamentals
Our Artificial Intelligence Fundamentals Certificate program is designed to give you flexibility in your study with instructor support and guidance. The program includes six courses total and each course is 4.5 quarter credit hours, which means you can earn up to a total of 27 quarter credit hours upon program completion. Each course is 5-weeks in length and can be taken independently or with a second course allowing you to complete the program in as little as 15 weeks.
Basic Front-End Development
Basic Back-End Development
Software Development Certificate: Full Stack (Full-Time)
In this course, you will study foundational concepts and software development skills necessary to learn any programming language. You will be introduced to topics including setting up the development environment, Git, GitHub, and the command line interface. You will also explore the importance of version control and the strengths of version control in your first few projects.
Front End Development
Database & Node.js Back End
In this course, you will study the foundation for back-end development. You will be introduced to tools such as databases (SQL and NoSQL), Express, and Node.js. You will study database basics, including creating a database, requesting data through queries, and how data is added, edited, and removed. You will explore how databases are tied to a web page to verify and authenticate users as well as retrieve specific user information.
React Framework & Redux
AI & Machine Learning Engineering Certificate
Our AI & Machine Learning Engineering Certificate program is designed to give you flexibility in your study with instructor support. The program includes six courses total and each course is 4.5 quarter credit hours, which means you can earn up to a total of 27 quarter credit hours upon program completion. Each course is 5-weeks in length and can be taken independently or with a second course allowing you to complete the program in as little as 15 weeks.
Introduction to Python Programming
This course is designed to provide students with a comprehensive introduction to Python, setting the foundation for writing programs for Artificial Intelligence and Machine Learning. Topics covered include data structures, functions, file handling, object-oriented-programming, classes, and AI-related Python libraries and frameworks.
Artificial Intelligence, Machine Learning, and Data Science Programming with Python
This course focuses on widely-used programming languages for AI, ML, and Data Science development. Topics include programming syntax, data structures, and libraries essential for AI programming. The course provides students with hands-on opportunities to put theory into practice.
This course delves into deep learning, with a focus on neural networks to include Convolutional Neural Networks and Recurrent Neural Networks. Additional topics include backpropagation, activation functions, and deep learning frameworks. The course also covers the concepts of transfer learning and model fine-tuning.
Natural Language Processing and Computer Vision
Natural Language Processing (NLP) is essential for AI applications involving text and language; and computer vision is crucial for AI applications involving images and videos. Both NLP and computer vision are covered in this course with topics such as language processing models, preprocessing, sentiment analysis, and named entity recognition, text generation, chatbot development, image processing, object detection, and image classification.
Reinforcement learning focuses on teaching machines to make decisions through trial and error. This course covers reinforcement learning algorithms and applications. It also features the Markov Decision Process (MDP), Q-Learning, Policy Gradients, and training AI agents.
Architecting AI Systems: Design Strategies and Best Practices
This course is designed to provide students with knowledge of how AI systems are architected, deployed, and supported. Specific design strategies and best practices are covered through the use of real-world case studies. Real-world scenarios are used to gain insights into architecting and deploying AI systems. Topics include deployment strategies, scalability, monitoring, maintaining, and ethical considerations.
AI & Machine Learning Engineering Bootcamp FAQs
Base tuition for all bootcamps is $13,500. A refundable $99 deposit is required to enroll.
The remaining tuition may be paid in full before class starts, with an installment plan while in class, or financed over 3 to 10 years through one of our lending partners (though you're welcome to use your own if you'd prefer).
Scholarships and other offers are available for those who qualify to help reduce costs.
Our online flex bootcamp program begins every five (5) weeks on Wednesdays. The length of time to complete the 27-quarter credit hour program will be dependent on the student’s progress and success in the program but can be completed in as little as 15 weeks.
Many of our students have success working full-time jobs while managing their program responsibilities. DigitalCrafts Flex instruction allows students to plan their bootcamp schedule on their own time, around work and other obligations, adhering to weekly deadlines.
Our programs provide online learning with live instructor guidance and feedback. The curriculum has been designed to effectively challenge beginners and experienced learners alike. Students can expect hands-on exercises, tools, and projects that reflect skills utilized in the workplace today. DigitalCrafts instructors and staff strive to be highly accessible and care deeply about their students.
We strongly recommend a recent (~4 years or newer) Macbook with at least 8GB of RAM. Our instructors are proficient with macOS and may not be able to assist you if you have issues with a non-macOS computer.
8GB of RAM minimum, 16GB recommended. At least 30GB of free storage space. Reliable broadband internet for streaming class. Camera and microphone (laptop built-in is fine).
Chromebooks and iPads are not sufficient.
DigitalCrafts offers students several options to pay their bootcamp tuition. For more information on those options, visit our tuition and financing page.
Flex instruction provides students with one-on-one access to their instructor with feedback and mentoring as they access learning materials and complete labs and assignments each week. They will study on their own time and create their own schedule for assignment completion adhering to weekly deadlines. Optional weekly live sessions with your instructor, assignment feedback, and regular check-ins with advisors are designed to ensure the student is supported throughout their academic journey.
- Review your syllabi carefully.
- Make note on a calendar of assignment deadlines.
- Attend live sessions with faculty.
- Schedule reminders to complete large assignments.
- Follow a schedule for learning - plan days and times to work on your class.
- Treat your study time as a non-breakable appointment with yourself.
- Participate in course discussion boards.
- Stay in touch with the student success team about the next steps.
- Find a quiet place to work