Register training material
27 materials found

Keywords: AI  or Python  or evaluation 


Tutorials to learn how to use STAN

Stan tutorials offer links to exceptional tutorial papers, videos and statistics to learn Bayesian statistical methods and applied statistics.

Keywords: Statistics, applied statistics, Bayesian statistics, R software, Python, MATLAB

Tutorials to learn how to use STAN https://dresa.org.au/materials/tutorials-to-learn-how-to-use-stan Stan tutorials offer links to exceptional tutorial papers, videos and statistics to learn Bayesian statistical methods and applied statistics. https://mc-stan.org/about/team/ Statistics, applied statistics, Bayesian statistics, R software, Python, MATLAB
WEBINAR: AlphaFold: what's in it for me?

This record includes training materials associated with the Australian BioCommons webinar ‘WEBINAR: AlphaFold: what’s in it for me?’. This webinar took place on 18 April 2023.

Event description 

AlphaFold has taken the scientific world by storm with the ability to accurately predict the...

Keywords: Bioinformatics, Machine Learning, Structural Biology, Proteins, Drug discovery, AlphaFold, AI, Artificial Intelligence, Deep learning

WEBINAR: AlphaFold: what's in it for me? https://dresa.org.au/materials/webinar-alphafold-what-s-in-it-for-me-4d1ea222-4240-4b68-b9ae-7769ac664ee0 This record includes training materials associated with the Australian BioCommons webinar ‘WEBINAR: AlphaFold: what’s in it for me?’. This webinar took place on 18 April 2023. Event description  AlphaFold has taken the scientific world by storm with the ability to accurately predict the structure of any protein in minutes using artificial intelligence (AI). From drug discovery to enzymes that degrade plastics, this promises to speed up and fundamentally change the way that protein structures are used in biological research.  Beyond the hype, what does this mean for structural biology as a field (and as a career)? Dr Craig Morton, Drug Discovery Lead at the CSIRO, is an early adopter of AlphaFold and has decades of expertise in protein structure / function, protein modelling, protein – ligand interactions and computational small molecule drug discovery, with particular interest in anti-infective agents for the treatment of bacterial and viral diseases. Craig joins this webinar to share his perspective on the implications of AlphaFold for science and structural biology. He will give an overview of how AlphaFold works, ways to access AlphaFold, and some examples of how it can be used for protein structure/function analysis. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/4ytn2_AiH8s Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Machine Learning, Structural Biology, Proteins, Drug discovery, AlphaFold, AI, Artificial Intelligence, Deep learning
NCI training strategy and impact story to address pressing needs from user community

This presentation looks at how NCI provides training opportunities, supporting users to develop their digital skills with the aim of underpinning the integrity of research. NCI's new training strategy is outlined along with how training is evaluated and impact is measured. Through extensive...

Keywords: training impact, evaluation, skills training, training strategy, community, survey design, training material

NCI training strategy and impact story to address pressing needs from user community https://dresa.org.au/materials/nci-training-strategy-and-impact-story-to-address-pressing-needs-from-user-community-70a328d3-a1e1-45ce-bac0-d8b896db4ca0 This presentation looks at how NCI provides training opportunities, supporting users to develop their digital skills with the aim of underpinning the integrity of research. NCI's new training strategy is outlined along with how training is evaluated and impact is measured. Through extensive internal polling the training team have identified a number of gaps. Based on the gaps an end-to-end learning journey has been created. The presentation also provides an overview of immediate, middle term and community impacts of training at NCI. You can watch the video on YouTube here:https://youtu.be/LVn5TZFufjI contact@ardc.edu.au training impact, evaluation, skills training, training strategy, community, survey design, training material
Evaluating training at Pawsey: Motivated, confident & "changed"

This presentation outlines the digital reserach skills training evaluation methods used at Pawsey. Using the Kirpatrick Training Evaluation model in designing their training evaluation survey, Pawsey measure learning motivation (How did the participant respond to the training?), improved...

Keywords: training impact, evaluation, behavioual change, survey design, skills training, motivation, confidence, training material

Evaluating training at Pawsey: Motivated, confident & "changed" https://dresa.org.au/materials/evaluating-training-at-pawsey-motivated-confident-changed-a2435a20-bfa5-457d-afa3-08193a199f24 This presentation outlines the digital reserach skills training evaluation methods used at Pawsey. Using the Kirpatrick Training Evaluation model in designing their training evaluation survey, Pawsey measure learning motivation (How did the participant respond to the training?), improved confidence of the learner (Did participants understand the training?) and were there any behavioual changes (How participants applied their new knowledge in practice?). You can watch the video of the presentation on YouTube here: https://youtu.be/IOKVrBumEBQ contact@ardc.edu.au training impact, evaluation, behavioual change, survey design, skills training, motivation, confidence, training material
Accelerating skills development in Data science and AI at scale

At the Monash Data Science and AI  platform, we believe that upskilling our research community and building a workforce with data science skills are key to accelerating the application of data science in research. To achieve this, we create and leverage new and existing training capabilities...

Keywords: AI, machine learning, eresearch skills, training, train the trainer, volunteer instructors, training partnerships, training material

Accelerating skills development in Data science and AI at scale https://dresa.org.au/materials/accelerating-skills-development-in-data-science-and-ai-at-scale-2d8a65fa-f96e-44ad-a026-cfae3f38d128 At the Monash Data Science and AI  platform, we believe that upskilling our research community and building a workforce with data science skills are key to accelerating the application of data science in research. To achieve this, we create and leverage new and existing training capabilities within and outside Monash University. In this talk, we will discuss the principles and purpose of establishing collaborative models to accelerate skills development at scale. We will talk about our approach to identifying gaps in the existing skills and training available in data science, key areas of interest as identified by the research community and various sources of training available in the marketplace. We will provide insights into the collaborations we currently have and intend to develop in the future within the university and also nationally. The talk will also cover our approach as outlined below •        Combined survey of gaps in skills and trainings for Data science and AI •        Provide seats to partners •        Share associate instructors/helpers/volunteers •        Develop combined training materials •        Publish a repository of open source trainings •        Train the trainer activities •        Establish a network of volunteers to deliver trainings at their local regions Industry plays a significant role in making some invaluable training available to the research community either through self learning platforms like AWS Machine Learning University or Instructor led courses like NVIDIA Deep Learning Institute. We will discuss how we leverage our partnerships with Industry to bring these trainings to our research community. Finally, we will discuss how we map our training to the ARDC skills roadmap and how the ARDC platforms project “Environments to accelerate Machine Learning based Discovery” has enabled collaboration between Monash University and University of Queensland to develop and deliver training together. contact@ardc.edu.au AI, machine learning, eresearch skills, training, train the trainer, volunteer instructors, training partnerships, training material
Intersect: Training portfolio

This presentation explores Intersect's training evaluation model. Short term evaluation for immediate satisfaction and value of the training. Long term evaluation methods with a specific survey design to determine behavioural change and impact over time of the training on researchers' workflows,...

Keywords: training impact, evaluation, short-term evaluation, long-term evaluation, behavioural change, survey design, skills training, training material

Intersect: Training portfolio https://dresa.org.au/materials/intersect-training-portfolio-8c82060d-5eea-4091-af7b-396c9c073dab This presentation explores Intersect's training evaluation model. Short term evaluation for immediate satisfaction and value of the training. Long term evaluation methods with a specific survey design to determine behavioural change and impact over time of the training on researchers' workflows, use of support services post training and looking for links between digital tools/technologies training and research outputs and grants. You can watch the full video on YouTube here: https://youtu.be/J3tCC-t_eO4 contact@ardc.edu.au training impact, evaluation, short-term evaluation, long-term evaluation, behavioural change, survey design, skills training, training material
Monash University - University of Queensland training partnership in Data science and AI

We describe the peer network exchange for training that has been recently created via an ARDC funded partnership between Monash University and Universities of Queensland under the umbrella of the Queensland Cyber Infrastructure Foundation (QCIF). As part of a training program in machine learning,...

Keywords: data skills, training partnerships, data science, AI, training material

Monash University - University of Queensland training partnership in Data science and AI https://dresa.org.au/materials/monash-university-university-of-queensland-training-partnership-in-data-science-and-ai-8082bf73-d20f-4214-ad8c-95123e25a36c We describe the peer network exchange for training that has been recently created via an ARDC funded partnership between Monash University and Universities of Queensland under the umbrella of the Queensland Cyber Infrastructure Foundation (QCIF). As part of a training program in machine learning, visualisation, and computing tools, we have established a series of over 20 workshops over the year where either Monash or QCIF hosts the event for some 20-40 of their researchers and students, while some 5 places are offered to participants from the other institution. In the longer term we aim to share material developed at one institution and have trainers present it at the other. In this talk we will describe the many benefits we have found to this approach including access to a wider range of expertise in several rapidly developing fields, upskilling of trainers, faster identification of emerging training needs, and peer learning for trainers. contact@ardc.edu.au data skills, training partnerships, data science, AI, training material
ARDC Skills Impact and Strategy Community Discussion

The focus of this community event arose from the ARDC SKills Summit 2021, hosted in collaboration with eResearch Australasia Conference. Two key themes identified at the Summit formed the focus of this event: 1) How to convince senior management the value of digital skills training so that they...

Keywords: training impact, evaluation, skills training, resourcing, value proposition, training material

ARDC Skills Impact and Strategy Community Discussion https://dresa.org.au/materials/ardc-skills-impact-and-strategy-community-discussion-e9d63cee-0d9c-4f8d-9c0f-58afe99b649b The focus of this community event arose from the ARDC SKills Summit 2021, hosted in collaboration with eResearch Australasia Conference. Two key themes identified at the Summit formed the focus of this event: 1) How to convince senior management the value of digital skills training so that they don't question resourcing 2) Evaluating the long-term impact of digital skills training on researchers’ workflows and outputs. You can watch the full video presentation on YouTube here: https://youtu.be/iSnE7OBILqs contact@ardc.edu.au training impact, evaluation, skills training, resourcing, value proposition, training material
WEBINAR: AlphaFold: what's in it for me?

This record includes training materials associated with the Australian BioCommons webinar ‘WEBINAR: AlphaFold: what’s in it for me?’. This webinar took place on 18 April 2023.

Event description 

AlphaFold has taken the scientific world by storm with the ability to accurately predict the...

Keywords: Bioinformatics, Machine Learning, Structural Biology, Proteins, Drug discovery, AlphaFold, AI, Artificial Intelligence, Deep learning

WEBINAR: AlphaFold: what's in it for me? https://dresa.org.au/materials/webinar-alphafold-what-s-in-it-for-me This record includes training materials associated with the Australian BioCommons webinar ‘WEBINAR: AlphaFold: what’s in it for me?’. This webinar took place on 18 April 2023. Event description  AlphaFold has taken the scientific world by storm with the ability to accurately predict the structure of any protein in minutes using artificial intelligence (AI). From drug discovery to enzymes that degrade plastics, this promises to speed up and fundamentally change the way that protein structures are used in biological research.  Beyond the hype, what does this mean for structural biology as a field (and as a career)? Dr Craig Morton, Drug Discovery Lead at the CSIRO, is an early adopter of AlphaFold and has decades of expertise in protein structure / function, protein modelling, protein – ligand interactions and computational small molecule drug discovery, with particular interest in anti-infective agents for the treatment of bacterial and viral diseases. Craig joins this webinar to share his perspective on the implications of AlphaFold for science and structural biology. He will give an overview of how AlphaFold works, ways to access AlphaFold, and some examples of how it can be used for protein structure/function analysis. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Materials shared elsewhere: A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/4ytn2_AiH8s Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Machine Learning, Structural Biology, Proteins, Drug discovery, AlphaFold, AI, Artificial Intelligence, Deep learning
Introduction to Jupyter Notebooks

This workshop will introduce you to Jupyter Notebooks, a digital tool that has exploded in popularity in recent years for those working with data.

You will learn what they are, what they do and why you might like to use them. It is an introductory set of lessons for those who are brand new,...

Keywords: jupyter, Introductory, training material, CloudStor, markdown, Python, R

Resource type: tutorial

Introduction to Jupyter Notebooks https://dresa.org.au/materials/introduction-to-jupyter-notebooks This workshop will introduce you to Jupyter Notebooks, a digital tool that has exploded in popularity in recent years for those working with data. You will learn what they are, what they do and why you might like to use them. It is an introductory set of lessons for those who are brand new, have little or no knowledge of coding and computational methods in research. This workshop is targeted at those who are absolute beginners or ‘tech-curious’. It includes a hands-on component, using basic programming commands, but requires no previous knowledge of programming. sara.king@aarnet.edu.au Mason, Ingrid jupyter, Introductory, training material, CloudStor, markdown, Python, R
Collecting Web Data

Web scraping is a technique for extracting information from websites. This can be done manually but it is usually faster, more efficient and less error-prone if it can be automated.

Web scraping allows you to convert non-tabular or poorly structured data into a usable, structured format, such as...

Keywords: Data Management, Python

Collecting Web Data https://dresa.org.au/materials/collecting-web-data Web scraping is a technique for extracting information from websites. This can be done manually but it is usually faster, more efficient and less error-prone if it can be automated. Web scraping allows you to convert non-tabular or poorly structured data into a usable, structured format, such as a .csv file or spreadsheet. But scraping is about more than just acquiring data: it can help you track changes to data online, and help you archive data. In short, it's a skill worth learning. So join us for this web scraping workshop to learn web scraping, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - The concept of structured data - The use of XPath queries on HTML document - How to scrape data using browser extensions - How to scrape using Python and Scrapy - How to automate the scraping of multiple web pages #### Prerequisites: A good knowledge of the basic concepts and techniques in Python. Consider taking our [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to come up to speed beforehand. **For more information, please click [here](https://intersect.org.au/training/course/webdata201).** training@intersect.org.au Data Management, Python
Learn to Program: Python

Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.

We teach using Jupyter notebooks, which allow program code, results,...

Keywords: Programming, Python

Learn to Program: Python https://dresa.org.au/materials/learn-to-program-python Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to the JupyterLab interface for programming - Basic syntax and data types in Python - How to load external data into Python - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data in Python #### Prerequisites: No prior experience with programming is needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/python101).** training@intersect.org.au Programming, Python
Python for Research

Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.

This workshop is an introduction to data structures (DataFrames using...

Keywords: Programming, Python

Python for Research https://dresa.org.au/materials/python-for-research Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data. This workshop is an introduction to data structures (DataFrames using the pandas library) and visualisation (using the matplotlib library) in Python. The targeted audience for this workshop is researchers who are already familiar with the basic concepts in programming such as loops, functions, and conditionals. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to Libraries and Built-in Functions in Python - Introduction to DataFrames using the pandas library - Reading and writing data in DataFrames - Selecting values in DataFrames - Quick introduction to Plotting using the matplotlib library #### Prerequisites: [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or any of the [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Learn to Program: MATLAB](https://intersect.org.au/training/course/matlab101/) or [Learn to Program: Julia](https://intersect.org.au/training/course/julia101/), needed to attend this course. If you already have some experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) course to ensure that you are familiar with the knowledge needed for this course. **For more information, please click [here](https://intersect.org.au/training/course/python110).** training@intersect.org.au Programming, Python
Data Manipulation in Python

Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.

In this workshop, you will explore DataFrames in depth (using the...

Keywords: Programming, Python

Data Manipulation in Python https://dresa.org.au/materials/data-manipulation-in-python Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data. In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Working with pandas DataFrames - Indexing, slicing and subsetting in pandas DataFrames - Missing data values - Combine multiple pandas DataFrames #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to ensure that you are familiar with the knowledge needed for this course. **For more information, please click [here](https://intersect.org.au/training/course/python201).** training@intersect.org.au Programming, Python
Data Visualisation in Python

Course Materials

You'll learn:

  • Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries
  • Configuring plot elements within seaborn and matplotlib
  • Exploring different types of plots using seaborn

Prerequisites:

Either [Learn to...

Keywords: Programming, Python

Data Visualisation in Python https://dresa.org.au/materials/data-visualisation-in-python Course Materials #### You'll learn: - Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries - Configuring plot elements within seaborn and matplotlib - Exploring different types of plots using seaborn #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to ensure that you are familiar with the knowledge needed for this course. We also strongly recommend attending the [Data Manipulation in Python](https://intersect.org.au/training/course/python201/). **For more information, please click [here](https://intersect.org.au/training/course/python202).** training@intersect.org.au Programming, Python
Data Manipulation and Visualisation in Python

Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data.

In this workshop, you will explore DataFrames in depth (using the...

Keywords: Programming, Python

Data Manipulation and Visualisation in Python https://dresa.org.au/materials/data-manipulation-and-visualisation-in-python Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data. In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets. You will also explore different types of graphs and learn how to customise them using two of the most popular plotting libraries in Python, matplotlib and seaborn (Data Visualisation). We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Working with pandas DataFrames - Indexing, slicing and subsetting in pandas DataFrames - Missing data values - Combine multiple pandas DataFrames - Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries - Configuring plot elements within seaborn and matplotlib - Exploring different types of plots using seaborn #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to ensure that you are familiar with the knowledge needed for this course. **For more information, please click [here](https://intersect.org.au/training/course/python203).** training@intersect.org.au Programming, Python
Introduction to Machine Learning using Python: Introduction & Linear Regression

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...

Keywords: Programming, Python

Introduction to Machine Learning using Python: Introduction & Linear Regression https://dresa.org.au/materials/introduction-to-machine-learning-using-python-introduction-linear-regression 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. #### You'll learn: - Understand the difference between supervised and unsupervised Machine Learning. - Understand the fundamentals of Machine Learning. - Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax and basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references 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. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - 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. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. 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: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python205).** training@intersect.org.au Programming, Python
Introduction to Machine Learning using Python: Classification

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...

Keywords: Programming, Python

Introduction to Machine Learning using Python: Classification https://dresa.org.au/materials/introduction-to-machine-learning-using-python-classification 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. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as 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. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references 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. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - 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. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. 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: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python206).** training@intersect.org.au Programming, Python
Introduction to Machine Learning using Python: SVM & Unsupervised Learning

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...

Keywords: Programming, Python

Introduction to Machine Learning using Python: SVM & Unsupervised Learning https://dresa.org.au/materials/introduction-to-machine-learning-using-python-svm-unsupervised-learning 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. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as 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. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references 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. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - 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. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. 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: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python207).** training@intersect.org.au Programming, Python
A showcase of Data Analysis in Python and R: A case study using COVID-19 data

In all fields of research we are being confronted with a deluge of data; data that needs cleaning and transformation to be used in further analysis. This webinar demonstrates the effective use of programming tools for an initial analysis of COVID-19 datasets, with examples using both R and...

Keywords: Programming, Python, R

A showcase of Data Analysis in Python and R: A case study using COVID-19 data https://dresa.org.au/materials/a-showcase-of-data-analysis-in-python-and-r-a-case-study-using-covid-19-data In all fields of research we are being confronted with a deluge of data; data that needs cleaning and transformation to be used in further analysis. This webinar demonstrates the effective use of programming tools for an initial analysis of COVID-19 datasets, with examples using both R and Python. #### You'll learn: - Cleaning up a dataset for analysis - Using Jupyter lab for interactive analysis - Making the most of the tidyverse (R) and pandas (python) - Simple data visualisation using ggplot (R) and seaborn (python) - Best practices for readable code #### Prerequisites: The webinar has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/coding002).** training@intersect.org.au Programming, Python, R
Start Coding without Hesitation: Programming Languages Showdown

Programming is becoming more and more popular, with many researchers using programming to perform data cleaning, data manipulation, data analytics, as well as creating publication quality plots. Programming can be really beneficial for automating processes and workflows. In this webinar, we are...

Keywords: Programming, Python, R, MATLAB, Julia

Start Coding without Hesitation: Programming Languages Showdown https://dresa.org.au/materials/start-coding-without-hesitation-programming-languages-showdown Programming is becoming more and more popular, with many researchers using programming to perform data cleaning, data manipulation, data analytics, as well as creating publication quality plots. Programming can be really beneficial for automating processes and workflows. In this webinar, we are exploring four of the most popular programming languages that are widely used in academia, namely Python, R, MATLAB, and Julia. #### You'll learn: - Why use Programming - An overview of Python, R, MATLAB, and Julia - Code comparison of the four programming languages - Popularity and job opportunities - Intersect's comparison - General guidelines on how to choose the best programming language for your research #### Prerequisites: The webinar has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/coding001).** training@intersect.org.au Programming, Python, R, MATLAB, Julia
NCI training strategy and impact story to address pressing needs from user community

This presentation looks at how NCI provides training opportunities, supporting users to develop their digital skills with the aim of underpinning the integrity of research. NCI's new training strategy is outlined along with how training is evaluated and impact is measured. Through extensive...

Keywords: training impact, evaluation, skills training, training strategy, community, survey design, training material

NCI training strategy and impact story to address pressing needs from user community https://dresa.org.au/materials/nci-training-strategy-and-impact-story-to-address-pressing-needs-from-user-community This presentation looks at how NCI provides training opportunities, supporting users to develop their digital skills with the aim of underpinning the integrity of research. NCI's new training strategy is outlined along with how training is evaluated and impact is measured. Through extensive internal polling the training team have identified a number of gaps. Based on the gaps an end-to-end learning journey has been created. The presentation also provides an overview of immediate, middle term and community impacts of training at NCI. You can watch the video on YouTube here:https://youtu.be/LVn5TZFufjI contact@ardc.edu.au training impact, evaluation, skills training, training strategy, community, survey design, training material
Evaluating training at Pawsey: Motivated, confident & "changed"

This presentation outlines the digital reserach skills training evaluation methods used at Pawsey. Using the Kirpatrick Training Evaluation model in designing their training evaluation survey, Pawsey measure learning motivation (How did the participant respond to the training?), improved...

Keywords: training impact, evaluation, behavioual change, survey design, skills training, motivation, confidence, training material

Evaluating training at Pawsey: Motivated, confident & "changed" https://dresa.org.au/materials/evaluating-training-at-pawsey-motivated-confident-changed This presentation outlines the digital reserach skills training evaluation methods used at Pawsey. Using the Kirpatrick Training Evaluation model in designing their training evaluation survey, Pawsey measure learning motivation (How did the participant respond to the training?), improved confidence of the learner (Did participants understand the training?) and were there any behavioual changes (How participants applied their new knowledge in practice?). You can watch the video of the presentation on YouTube here: https://youtu.be/IOKVrBumEBQ contact@ardc.edu.au training impact, evaluation, behavioual change, survey design, skills training, motivation, confidence, training material
Intersect: Training portfolio

This presentation explores Intersect's training evaluation model. Short term evaluation for immediate satisfaction and value of the training. Long term evaluation methods with a specific survey design to determine behavioural change and impact over time of the training on researchers' workflows,...

Keywords: training impact, evaluation, short-term evaluation, long-term evaluation, behavioural change, survey design, skills training, training material

Intersect: Training portfolio https://dresa.org.au/materials/intersect-training-portfolio This presentation explores Intersect's training evaluation model. Short term evaluation for immediate satisfaction and value of the training. Long term evaluation methods with a specific survey design to determine behavioural change and impact over time of the training on researchers' workflows, use of support services post training and looking for links between digital tools/technologies training and research outputs and grants. You can watch the full video on YouTube here: https://youtu.be/J3tCC-t_eO4 contact@ardc.edu.au training impact, evaluation, short-term evaluation, long-term evaluation, behavioural change, survey design, skills training, training material
ARDC Skills Impact and Strategy Community Discussion

The focus of this community event arose from the ARDC SKills Summit 2021, hosted in collaboration with eResearch Australasia Conference. Two key themes identified at the Summit formed the focus of this event: 1) How to convince senior management the value of digital skills training so that they...

Keywords: training impact, evaluation, skills training, resourcing, value proposition, training material

ARDC Skills Impact and Strategy Community Discussion https://dresa.org.au/materials/ardc-skills-impact-and-strategy-community-discussion The focus of this community event arose from the ARDC SKills Summit 2021, hosted in collaboration with eResearch Australasia Conference. Two key themes identified at the Summit formed the focus of this event: 1) How to convince senior management the value of digital skills training so that they don't question resourcing 2) Evaluating the long-term impact of digital skills training on researchers’ workflows and outputs. You can watch the full video presentation on YouTube here: https://youtu.be/iSnE7OBILqs contact@ardc.edu.au training impact, evaluation, skills training, resourcing, value proposition, training material
Accelerating skills development in Data science and AI at scale

At the Monash Data Science and AI  platform, we believe that upskilling our research community and building a workforce with data science skills are key to accelerating the application of data science in research. To achieve this, we create and leverage new and existing training capabilities...

Keywords: AI, machine learning, eresearch skills, training, train the trainer, volunteer instructors, training partnerships, training material

Accelerating skills development in Data science and AI at scale https://dresa.org.au/materials/accelerating-skills-development-in-data-science-and-ai-at-scale At the Monash Data Science and AI  platform, we believe that upskilling our research community and building a workforce with data science skills are key to accelerating the application of data science in research. To achieve this, we create and leverage new and existing training capabilities within and outside Monash University. In this talk, we will discuss the principles and purpose of establishing collaborative models to accelerate skills development at scale. We will talk about our approach to identifying gaps in the existing skills and training available in data science, key areas of interest as identified by the research community and various sources of training available in the marketplace. We will provide insights into the collaborations we currently have and intend to develop in the future within the university and also nationally. The talk will also cover our approach as outlined below •        Combined survey of gaps in skills and trainings for Data science and AI •        Provide seats to partners •        Share associate instructors/helpers/volunteers •        Develop combined training materials •        Publish a repository of open source trainings •        Train the trainer activities •        Establish a network of volunteers to deliver trainings at their local regions Industry plays a significant role in making some invaluable training available to the research community either through self learning platforms like AWS Machine Learning University or Instructor led courses like NVIDIA Deep Learning Institute. We will discuss how we leverage our partnerships with Industry to bring these trainings to our research community. Finally, we will discuss how we map our training to the ARDC skills roadmap and how the ARDC platforms project “Environments to accelerate Machine Learning based Discovery” has enabled collaboration between Monash University and University of Queensland to develop and deliver training together. contact@ardc.edu.au AI, machine learning, eresearch skills, training, train the trainer, volunteer instructors, training partnerships, training material
Monash University - University of Queensland training partnership in Data science and AI

We describe the peer network exchange for training that has been recently created via an ARDC funded partnership between Monash University and Universities of Queensland under the umbrella of the Queensland Cyber Infrastructure Foundation (QCIF). As part of a training program in machine learning,...

Keywords: data skills, training partnerships, data science, AI, training material

Monash University - University of Queensland training partnership in Data science and AI https://dresa.org.au/materials/monash-university-university-of-queensland-training-partnership-in-data-science-and-ai We describe the peer network exchange for training that has been recently created via an ARDC funded partnership between Monash University and Universities of Queensland under the umbrella of the Queensland Cyber Infrastructure Foundation (QCIF). As part of a training program in machine learning, visualisation, and computing tools, we have established a series of over 20 workshops over the year where either Monash or QCIF hosts the event for some 20-40 of their researchers and students, while some 5 places are offered to participants from the other institution. In the longer term we aim to share material developed at one institution and have trainers present it at the other. In this talk we will describe the many benefits we have found to this approach including access to a wider range of expertise in several rapidly developing fields, upskilling of trainers, faster identification of emerging training needs, and peer learning for trainers. contact@ardc.edu.au data skills, training partnerships, data science, AI, training material