Introduction to Machine Learning using Python: SVM & Unsupervised Learning at Deakin Online

Intersect is a pivotal part of Australian research landscapeWe provide robust, innovative services and collaborative technology to support world-class research at our member organisations and in the wider research communityIntersect delivers data storage, compute and analysis platforms, custom engineering, expert consulting and training programs to thousands of researchers every yearIntersect works closely with the ARDC (Australian Research Data Commons) built from ANDS, Nectar, and RDSIntersect is a member of the Software Carpentry Foundation, the NCI (National Computational Infrastructure) and the AAF (Australian Access Federation)

Intersect Australia

A comprehensive introduction to Support Vector Machine and Unsupervised models in Machine Learning

<p>Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. We provide a comprehensive introduction to Support Vector Machine and Unsupervised models in Machine Learning and use Python to apply the knowledge on real-world datasets. We hope after this hands-on workshop, you will have a better understanding of these Machine Learning models and techniques and appreciate its capability, as well as make better informed decisions on how to leverage Machine Learning in your research.</p><p></p><h3>Learning Outcomes:</h3><ul><li>Comprehensive introduction to Machine Learning models and techniques such as Logistic Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction.</li><li>Know the differences between various core Machine Learning models.</li><li>Understand the Machine Learning modelling workflows.</li><li>Use Python and scikit-learn to process real datasets, train and apply Machine Learning models</li></ul><h3></h3><h3>Prerequisites</h3><ul><li><strong>Either Learn to Program: Python, Data Manipulation in Python and Introduction to ML using Python: Introduction &amp; Linear Regression or Learn to Program: Python, Data Manipulation and Visualisation in Python and Introduction to ML using Python: Introduction &amp; Linear Regression </strong>needed to attend this course. If you already have experience with programming, please check the topics covered in the <a href="https://intersect.org.au/training/course/python101" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Learn to Program: Python</a>, <a href="https://intersect.org.au/training/course/python201" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Data Manipulation in Python</a>, <a href="https://intersect.org.au/training/course/python203" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Data Manipulation and Visualisation in Python</a> and <a href="https://intersect.org.au/training/course/python205" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Introduction to ML using Python: Introduction &amp; Linear Regression</a> courses to ensure that you are familiar with the knowledge needed for this course, such as <strong>good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training.</strong></li><li>Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.</li><li><strong>For more information about prerequisites and programming pathways for this course, please visit </strong><a href="https://intersect.org.au/training/course/python207" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer"><strong>the course page</strong></a><strong> on our website.</strong></li></ul><p></p><h3>Why do this course?</h3><ul><li>Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources.</li><li>It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning.</li><li>We do have applications on real datasets!</li><li>Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects.</li><li>Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning.</li></ul><p>For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops:</p><ul><li><a href="https://intersect.org.au/training/course/python205/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Introduction to Machine Learning using Python: Introduction &amp; Linear Regression</a></li><li><a href="https://intersect.org.au/training/course/python206/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Introduction to Machine Learning using Python: Classification</a></li><li><a href="https://intersect.org.au/training/course/python207/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Introduction to Machine Learning using Python: SVM &amp; Unsupervised Learning</a></li></ul><p></p><h3>The Intersect approach to training</h3><p>At Intersect, we deliver hands-on courses  that targets the day-to-day software and technology problems that researchers face. </p><p>For more information about <a href="https://intersect.org.au/training/course/python207" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">this course</a> and others, see our <a href="https://intersect.org.au/training/courses" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">course catalogue</a>, or visit <a href="http://learn.intersect.org.au" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer"><strong>Learn.intersect.org.au</strong></a></p><p>For more information about how we allocate tickers and address no-shows, see our <a href="https://intersect.org.au/policy/training/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">training policy</a>.</p>

Our {communityGuidelinesLink} describe the sort of content we prohibit on Eventbrite. If you suspect an event may be in violation, you can report it to us so we can investigate.

To report other categories of prohibited or illegal content, submit {link}

Introduction to Machine Learning using Python: SVM & Unsupervised Learning at Deakin Online

More events from Intersect Australia

Follow organisers to get events picked for you

Introduction to Machine Learning using Python: SVM & Unsupervised Learning at Deakin Online

More events from Intersect Australia

Follow organisers to get events picked for you

You might also like...

Sign in for personalised recommendations