Introduction to Machine Learning (November 2023) About the Course Join our in-person comprehensive data upskilling short course designed to teach you fundamental data skills and applied machine learning in Python using health data. Whether you're already working with data and datasets or seeking to reskill, this course provides the foundations in machine learning you need. The demand for fundamental data skills and applied machine learning expertise is rapidly growing across all sectors. Python is one of the most popular programming languages, so you'll acquire sought-after skills that are applicable in industries such as web development, desktop applications, and healthcare. Our experienced instructors, with diverse backgrounds in data handling, will guide you throughout the course. We provide foundational skills tailored to the backgrounds of attendees, ensuring you gain the knowledge needed to work with data in various sectors. The lesson materials, maintained by the community and available under a CC-BY license, support your learning journey. While prior knowledge of Python, such as that gained through a Carpentries course, can be beneficial, it is not required. Don't miss this opportunity to enhance your data skills and delve into applied machine learning in Python. Join us and unlock new possibilities in the field of data analysis and prediction using health data. Who is the course for? Though there are no specific entry requirements, learners will find some prior Python knowledge beneficial. You must be comfortable studying and learning in English if it is not your first language. Learning Outcomes Our four lessons on applied machine learning will guide you through a typical pipeline for prediction, covering essential concepts in data preparation, model training, and performance evaluation. Decision trees, neural networks, and responsible use of models are among the key topics emphasized. Through practical, hands-on workshops, you’ll work real and messy data. You’ll identify, diagnose, and treat data-cleaning problems, apply appropriate machine learning algorithms to your problem and write scripts covering the end-to-end machine learning pipeline. How will this course be delivered? This is a 2 day, in-person course, comprising a total of 12 hours study from 10am - 5pm each day. Course fees and funding Course fees for 23/24 are £240 but funded places may be available for people employed or unemployed in Scotland (residency requirements apply). Funding Funding through the Scottish Funding Council (SFC) Upskilling Fund is still to be confirmed but prospective students are encouraged to apply in the event that it will later become available Eligibility Funded places may be available to those who meet SFC fee waiver criteria: “Courses/provision is open to all Scottish-domiciled/’home fee’ students, which is consistent with SFC’s policy for core funded student places. Students from the rest of the UK (rUK) are not normally considered eligible for SFC funding. If however a university is working with a Scottish/UK employer which has a physical presence or operating in Scotland, rUK employees of that employer would be eligible.” If you are from outside Scotland, you need to have settled status in the UK and meet other residency criteria: be ordinarily resident in the United Kingdom, the Channel Islands or the Isle of Man for the three years immediately before course start date, and have ‘settled status’ in the UK (as set out in the Immigration Act 1971) at the course start date, and be ordinarily resident in Scotland at the course start date. You can find out more about residency criteria on the SAAS website or in this summary Funding eligibility will be assessed at the point of each application for each course; you may be asked to provide further information if you do not meet the general residence conditions. You can check your likely fee status here. Please email us at upskilling@ed.ac.uk if you would like to discuss your funding eligibility before applying. Please note that full-time students (including full-time PhD students) are not eligible for funding. What is received upon completion? You will receive a certificate of completion. How to apply We have now reached capacity for places on this course. Applications for November 2023 are closed. Nov 13 2023 00.00 - Nov 14 2023 23.59 Introduction to Machine Learning (November 2023) Applied machine learning in Python using health data. This in-person course covers a typical pipeline for prediction from preparing data through to evaluating performance. Previous Python knowledge is beneficial.
Introduction to Machine Learning (November 2023) About the Course Join our in-person comprehensive data upskilling short course designed to teach you fundamental data skills and applied machine learning in Python using health data. Whether you're already working with data and datasets or seeking to reskill, this course provides the foundations in machine learning you need. The demand for fundamental data skills and applied machine learning expertise is rapidly growing across all sectors. Python is one of the most popular programming languages, so you'll acquire sought-after skills that are applicable in industries such as web development, desktop applications, and healthcare. Our experienced instructors, with diverse backgrounds in data handling, will guide you throughout the course. We provide foundational skills tailored to the backgrounds of attendees, ensuring you gain the knowledge needed to work with data in various sectors. The lesson materials, maintained by the community and available under a CC-BY license, support your learning journey. While prior knowledge of Python, such as that gained through a Carpentries course, can be beneficial, it is not required. Don't miss this opportunity to enhance your data skills and delve into applied machine learning in Python. Join us and unlock new possibilities in the field of data analysis and prediction using health data. Who is the course for? Though there are no specific entry requirements, learners will find some prior Python knowledge beneficial. You must be comfortable studying and learning in English if it is not your first language. Learning Outcomes Our four lessons on applied machine learning will guide you through a typical pipeline for prediction, covering essential concepts in data preparation, model training, and performance evaluation. Decision trees, neural networks, and responsible use of models are among the key topics emphasized. Through practical, hands-on workshops, you’ll work real and messy data. You’ll identify, diagnose, and treat data-cleaning problems, apply appropriate machine learning algorithms to your problem and write scripts covering the end-to-end machine learning pipeline. How will this course be delivered? This is a 2 day, in-person course, comprising a total of 12 hours study from 10am - 5pm each day. Course fees and funding Course fees for 23/24 are £240 but funded places may be available for people employed or unemployed in Scotland (residency requirements apply). Funding Funding through the Scottish Funding Council (SFC) Upskilling Fund is still to be confirmed but prospective students are encouraged to apply in the event that it will later become available Eligibility Funded places may be available to those who meet SFC fee waiver criteria: “Courses/provision is open to all Scottish-domiciled/’home fee’ students, which is consistent with SFC’s policy for core funded student places. Students from the rest of the UK (rUK) are not normally considered eligible for SFC funding. If however a university is working with a Scottish/UK employer which has a physical presence or operating in Scotland, rUK employees of that employer would be eligible.” If you are from outside Scotland, you need to have settled status in the UK and meet other residency criteria: be ordinarily resident in the United Kingdom, the Channel Islands or the Isle of Man for the three years immediately before course start date, and have ‘settled status’ in the UK (as set out in the Immigration Act 1971) at the course start date, and be ordinarily resident in Scotland at the course start date. You can find out more about residency criteria on the SAAS website or in this summary Funding eligibility will be assessed at the point of each application for each course; you may be asked to provide further information if you do not meet the general residence conditions. You can check your likely fee status here. Please email us at upskilling@ed.ac.uk if you would like to discuss your funding eligibility before applying. Please note that full-time students (including full-time PhD students) are not eligible for funding. What is received upon completion? You will receive a certificate of completion. How to apply We have now reached capacity for places on this course. Applications for November 2023 are closed. Nov 13 2023 00.00 - Nov 14 2023 23.59 Introduction to Machine Learning (November 2023) Applied machine learning in Python using health data. This in-person course covers a typical pipeline for prediction from preparing data through to evaluating performance. Previous Python knowledge is beneficial.
Nov 13 2023 00.00 - Nov 14 2023 23.59 Introduction to Machine Learning (November 2023) Applied machine learning in Python using health data. This in-person course covers a typical pipeline for prediction from preparing data through to evaluating performance. Previous Python knowledge is beneficial.
