A three-hour introductory workshop for newcomers to Julia and machine learning. Participants will have training in some technical domain, for example, in science, economics or engineering. However, no prior experience with Julia or machine learning is needed. Ask questions during the workshop: https://pigeonhole.at/JULIA2 Make sure to register for JuliaCon to get access to all of the resources: https://juliacon.org/2022/tickets/ In their simplest manifestation, machine learning algorithms extract, or "learn", from historical data some essential properties enabling them to respond intelligently to new data (typically, automatically). For example, spam filters predict whether to designate a new email as "junk", based on how a user previously designated a large number of previous messages. A property valuation site suggests the sale price for a new home, given its location and other attributes, based on a database of previous sales. Julia is uniquely positioned to accelerate developments in machine learning and there has been an explosion of Julia machine learning libraries. MLJ (Machine Learning in Julia) is a popular toolbox providing a common interface for interacting with over 180 machine learning models written in Julia and other languages. This workshop will introduce basic machine learning concepts, and walk participants through enough Julia to get started using MLJ. Prerequisites: - Essential. A computer with Julia 1.7.3 installed. - Strongly recommended, Workshop resources pre-installed. See here: https://github.com/ablaom/HelloJulia.jl/wiki/JuliaCon-2022-workshop:-Getting-started-with-Julia-and-MLJ. - Recommended. Basic linear algebra and statistics, such as covered in first year university courses. - Recommended but not essential. Prior experience with a scripting language, such as python, MATLAB or R. Resources Code used in the workshops: https://github.com/ablaom/HelloJulia.jl Pluto.jl home page: https://plutojl.org/ Pluto.jl package repository: https://github.com/fonsp/Pluto.jl Contents 00:00, Opening and introduction 02:14, 0. Outline 05:10, 1. Workshop resources 09:40, 2. Machine Learning 11:35, 2.1. Supervised Learning 20:56, 2.1.1. Survival of Passengers on the Titanic 24:45, 3.1. Begin of Coding (Tutorial 1) 41:53, 3.1.1. Functions 46:30, 3.1.2. Iterate 56:00, 3.1.3. Pluto.jl notebook 1:01:42, 3.1.4. Probability Distributions 1:08:48, 3.1.5. Plotting 1:13:30, 3.2. Tutorial 2: Dataframe 1:20:39, Skip Coffee Break 1:28:09, OpenML 1:30:06, 3.2.1. Grabbing the Titanic dataset 1:40:40, 3.3. Tutorial 3: Machine Learning 1:40:43, 3.3.1. Scitype 1:46:43, 3.3.1. Titanic data 2:09:30, 3.3.2. Splitting data into train and test sets 2:13:13, 3.3.3. Cleaning data 2:22:47, 3.3.4. Splitting data into input features and target 2:28:05, 3.3.5. Choosing model 2:33:04, 3.3.6. The fit/predict worflow 2:39:00, 4. Q&A S/O to https://github.com/ktzhao for the video timestamps! Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/JuliaCommunity/YouTubeVideoTimestamps Interested in improving the auto generated captions? Get involved here: https://github.com/JuliaCommunity/YouTubeVideoSubtitles