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Machine Learning for Data Scientists

Welcome to the cutting edge of Data Science. Learn how to build machine learning models, run data pipelines, make recommendation systems and deploy cloud-based solutions.

  • Starting 20 Jan 2020
  • 100% online, 12 weeks (3 months)
  • A$2,000 GST inc.
  • Prerequisites: Python programming, including common data analysis libraries (NumPy, Pandas, Matplotlib); SQL programming; Statistics (Descriptive and Inferential); Calculus; Linear Algebra; Experience wrangling and visualising data
 
 

100% online, mentor supported, workplace ready

Why learn Machine Learning for Data Scientists

IBM Watson’s Chief Architect, Paul Taylor, said it best: “AI and Data Science require new skills that are in high demand and ultimately impact all industries, organisations and modern smart systems.” He’s right, too. Data Science is evolving, and employers are putting a premium on machine learning experience. The McKinsey Global Institute estimates 200,000 data scientist job openings in 2019, with salary benchmarks at around $147,000. A working knowledge of AI isn’t a bonus anymore – it’s almost a necessity.

In this course, you’ll learn the fundamentals of machine learning from some of Australia’s leading industry experts, build basic algorithms, run pipelines and deploy your solutions to the cloud. Everything you need for a brave new world.

 

How does it work

Machine Learning For Data Science was designed with cutting-edge content from Silicon Valley disruptor Udacity, then multiplied with the credibility of RMIT through localised mentors with the best in industry.  It’s a technical, job-ready program. You’ll need to be comfortable with wrangling data in Python and SQL, and have some experience with inferential statistics and data visualization. (If you’re after an intro to Data Science, check out RMIT’s Programming For Data Science. 

Skills learned

Supervised and unsupervised machine learning, neural networks, data pipelines, recommendation systems, data scaling, PCA, PyTorch.

Find out more in our FAQ section.

Your experience with RMIT Future Skills

Upskill online, on your terms. Taught by industry leaders, backed by RMIT.

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100% online

Our flexible short courses are designed to fit your lifestyle, not replace it. Study online, after hours, when and where you like.

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RMIT certificate

The cutting edge skills you learn will be rigorously assessed and recognised by both a leading university and key employers in the field.

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Industry mentors

RMIT experience provides structure on top of online learning through 1:1 mentor support, webinars and a community of lifelong learners.

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Real world skills

Level up your skills and qualifications as a digital native. Our courses are about practical training, building a network and applying your learning.

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Expert content

We have partnered with leading employers across the world to multiply the power of a world-leading university with the best of industry.

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Community

Make the most out of your online learning by interacting with your peers, experts and industry employers. Meaningful connections to help propel your career.

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Learning outcomes

What do you take away from our courses?

Course outcome certificate

Qualification

After completing an RMIT Future Skills course, you will earn an RMIT certificate which can be validated, recognised and shared on social media platforms.

Skills

These courses are all about practical training. Each week, the skills you need to master will be walked through in live online tutorials that allow you to see success in action, and ask questions to overcome obstacles that are slowing your progress.

Work-ready project

We combine the power of industry experts with a leading university to get you working on practical, work-ready projects that you can immediately apply back in your office.

Course Outline

Module 1

Supervised learning

Types of machine learning

Regression

Perceptron algorithms

Decision trees

Naive Bayes

Support vector machines

Ensemble of learners

Evaluation metrics

Training and tuning

 

Project

Build an algorithm to find donors for a business

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Module 2

Introduction to Deep Learning

Introduction to neural networks

Implementing gradient decent

Training neural networks

Keras

Deep learning with PyTorch

Project

Create an image classifier

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Module 3

Unsupervised learning

Clustering

Hierarchical and density based clustering

Gaussian mixture models

Principal component analysis

Random projections and independent component analysis

Project

Create customer segments

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Learn with Industry Experts

Get ready to meet some of the biggest names in the business.

Margarita Moya logo

Margarita Moya

Senior analyst, Westpac

Margarita is a senior analyst with over 7 years experience across market research, analytics, and financial services. As a lead Tableau developer, her current role also focuses on advocating and empowering business users to use visual analytics, building up internal Tableau education programs and the nitty gritty details of server infrastructure. Passionate about people, data and the positive social impact the two combined can achieve, she’s also a current Board Member of Engineers Without Borders Australia and is registered on Tableau Service Corps.

Kale Temple logo

Kale Temple

Co-founder and practise director at Intellify

Kale Temple is a co-founder and practise director at Intellify where he leverages expertise in data science and machine learning to architect solutions that empower business performance and growth. He has consulted with a number of the world’s leading corporate and government organisations from over 5 years. Since 2014, he has co-founded and scaled two successful technology start-ups and as Data Scientist & Consultant at Agile BI, played a key role in building the business from the ground up into a world-leading Microsoft Power BI Partner. He holds a Bachelor of Liberal Arts and Sciences (Economics) and Masters of Economics (Economics & Econometrics) from the University of Sydney.


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