Past Meetup

DataHack 2018 Prep Night & ML Workshop (Hebrew)

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DataHack 2018 Prep Night and ML workshop!!! πŸŽ‰ πŸŽ‰ πŸŽ‰

Join us for a special preparation event for [DataHack 2018]
( at Samsung NEXT on Tuesday, September 25th!πŸŽ‰

β€’ We will explain everything about DataHack 2018 and give tips about what makes a good project. βœ”

β€’ Our sponsors will present the challenges for the hackathon. πŸ†

β€’ We will have a designated time for finding team members πŸ‘¨β€β€β€πŸ’‹β€πŸ‘¨ and presenting ideas. πŸ—―

β€’ A machine learning workshop (in Hebrew) that will get from zero to hero in no time! πŸ’ͺ🏻 See the description below...

β€’ There will be pizzas! πŸ•β—

18:00 - Mingling
18:30 - Presenting the hosts and the challenges
19:00 - Presenting teams and ideas
19:30 - Machine Learning Workshop (description bellow!)

(All content will be presented in Hebrew)

Introduction to Machine Learning with Python - Coding Your First Model

A track aimed at those new to Python programming or data science, it covers both basic theoretical background and practical skills required to successfully tackle your first data science project!
The content in this workshop covers the preliminary concepts & skills necessary for data science work. Although considered β€œpreliminary” (a must have) we will cover them with the depth necessary to fully understand and utilize later.
These include the data science workflow, ML task types (or - what can / should I do with ML), popular ML algorithms (models), gradient descent (what β€œtraining” a model means), avoiding overfitting (generalisation, complexity issues, validation etc.) and hyper parameter tuning.
We will go over simple python code for all of the above and later in the workshop use these to compete (together or alone) in a Kaggle competition to practice this for real.
* This workshop is a prerequisite for non data scientist planning to attend the more advanced In-Conf sessions of DataLearn.

Notes from your instructor, Daniel Marcous:
* The approach taught here for ML / data science is practical rather than theoretical. This means that for this workshop (and in my mind always) the intuition of how things work, and how/when to use them is far more important than the math behind them.
* I have already taught a very similar course at several occasions (and locations) @Google (where I work as a Data Wizard?!) and found this approach favourable to others.
* This is your chance to learn ML the same way that Google software engineers / analysts do!
* Feel free to approach me ([masked]) with every question/consideration, as little as possible, regarding this workshop or DataLearn in general.
* WARNING : you might get a taste for machine learning and not want to go back to your day job!