Fundamentals of Machine Learning

Eestikeelne lühitutvustus:

Masinõpe on andmeanalüüsi meetod, mis automatiseerib analüütilise mudeli koostamist. See on tehisintellekti haru, mis põhineb ideel, et süsteemid saavad andmetest õppida, mustreid tuvastada ja otsuseid langetada minimaalse inimese sekkumisega. Lihtsalt öeldes kasutatakse masinõppe algoritme, et leida mustreid suurtest andmemahtudest. Andmed hõlmavad kõiki numbreid, sõnu, pilte, klikke – ükskõik, mis andmed digitaalselt on, kõiki saab sööta masinõppe algoritmidele.

Masinõpet on võimalik rakendada igas ettevõttes, väikestest suurteni. Näiteks kasutavad masinõpet tuntud platvormid nagu Youtube, Netflix, Spotify, Facebook, Twitter – eesmärgiga pakkuda kasutajale temale paremat infovälja. Vaja on vaid andmebaase, mille kallal masinõpe toimetama panna. Täpsete mudelite loomisega on organisatsioonil parem võimalus tuvastada kasumlikud võimalused või vältida tundmatuid riske. 

Koolitus toimub inglise keeles!

This 3-day course will enable you to better understand what Machine Learning is, including the fundamental practices and principles. It is an introduction to R and data analytics, with a deeper Introduction to Machine Learning. At the end of the course you'll be able to create simple Machine Learning models using Python and R. 

Target audience: Aimed at people with existing technological and mathematical background looking to get a quick exposure to mathematics and techniques of Machine Learning.

The results of the training
At the end of this course attendees will know:

•          The fundamentals of Machine Learning methodologies and algorithms
•          The mathematics required for understanding and using Machine Learning algorithms
•          R Machine Learning packages

 At the end of this course attendees will be able to:

•          Build Machine Learning models using R
•          Perform regression analysis
•          Perform computer simulations using R
•          Perform validation of Machine Learning models using R to evaluate the quality of models

Prerequisites to the course (recommended):
Delegates wish to take this course should have already completed 'Fundamentals of Data Science' or have the equivalent level of knowledge.

Length: 24 academic hours

The prerequisite for issuing the certificate is full participation in training.

The training topics:

•          Intro to Science, Data Science and Big Data
•          Intro to Machine Learning
•          Intro to Mathematics
•          Intro to Statistics
•          Intro to Python
•          Intro to Python for Data Science

Machine Learning with Python

The training price also includes:
teaching materials;
a trainer's consultation on the topics learned by e-mail after the training;
certificate.

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