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Intro to Machine Learning

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.

Time commitment
12 weeks (3 months) 100% online
Next start date
20 Jan 2020
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
A$ 2000 incl GST
Enrol now

AUSTRALIAN INDUSTRY PARTNERS


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.

The freedom of online study

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Get RMIT certified

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

Course Structure

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

 

Module 2

Introduction to Deep Learning

Introduction to neural networks

Implementing gradient decent

Training neural networks

Keras

Deep learning with PyTorch

Module 3

Unsupervised learning

Clustering

Hierarchical and density based clustering

Gaussian mixture models

Principal component analysis

Random projections and independent component analysis

Learn with Industry Experts

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

Margarita Moya
Margarita MoyaSenior 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
Kale TempleCo-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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