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: [ Ссылка ]

Make sure to register for JuliaCon to get access to all of the resources: [ Ссылка ]

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: [ Ссылка ].
- 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: [ Ссылка ]
Pluto.jl home page: [ Ссылка ]
Pluto.jl package repository: [ Ссылка ]

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

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