Introduction to Machine Learning
This course will provide you with the foundations for exploring Machine Learning and Artificial Intelligence (AI). It focuses on using software to develop and implement machine learning techniques.
Why Monash College?
Monash College’s innovative education and digital skills programs are grounded in strong, evidence-based methodologies. The College is wholly owned by Monash University and has more than 25 years’ experience designing, developing, delivering and reviewing learner-centred programs. It works with 2,000 industry partners to deliver industry placements for 3,000 students annually.
As an established teaching institution, Monash College has a strong cohort of teaching professionals who hold minimum tertiary qualifications in their fields of specialisation and current links to industry.
Victorian Government Funding
The Victorian Government’s Digital Jobs program aims to build the state’s digital workforce by training and upskilling mid-career Victorians so they can transition into digital careers. Through Digital Jobs, 5,000 mid-career Victorians will complete a six-month program that includes 12 weeks of industry-backed training followed by 12 weeks in a digital job with a Victorian business.
Course information:
Mode of study | This course is taught asynchronously (self-paced), with 12 – 14 hours of learning per week. A course trainer will guide you through your learning. Weekly sessions will be scheduled and trainers will participate in forums and be available to answer questions and provide guidance. |
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Duration | 12 weeks |
Where can it lead | The skills learnt through this program will best complement your existing skills and experiences from a wide variety of professional fields. Together, these useful and transferable skills can support transition into entry-level or support roles in the following areas:
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Course load | 12 - 14 hours per week, including 2 hours of optional drop-in time with the trainer. |
Additional services |
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Download the Introduction to Machine Learning Unit Guide (PDF, 0.13 MB)