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Machine Learning for Applied Solutions Course 2018, Armenia

Machine Learning for Applied Solutions Course 2018, Armenia

Arménie 13 mars 2018

DÉTAILS OPPORTUNITÉ

Récompense totale
0 $
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Date limite
13 mars 2018
Niveau d'études
Type d'opportunité
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Financement complet
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Toutes les régions

Machine Learning for Applied Solutions

Become a Machine Learning Specialist in 21 weeks

Machine Learning is an interdisciplinary field and it’s hard to get into it without knowledge of mathematics and programming. ISTC  together with HiLearn team has created this new in depth course to help you gain the knowledge necessary to work in this field if you already know some programming or mathematics.

This course has been designed by professional Machine Learning Engineers and Specialists so that they will share their knowledge and help you learn programming, algorithms and machine learning in a simple way.

The course provides you with 21 weeks of highly valuable content.

The course starts with 2 parallel tracks that teach you the basic programming skills and fundamental mathematics necessary to get started with the course. Afterwards, there is a mixture of python and mathematics to get you started with the necessary tools to convert theoretical machine learning knowledge to software. The main part of the course will teach you both the mathematical foundations of different ML models and give you hands-on programming assignments to convert those into working software. Finally the last stage will be supervised by different partners that will give you real world projects to sharpen your skills and get acquainted with industrial software engineering workflows.

Before taking part in the program, applicants will pass a preliminary test exam based on programming and mathematical directions.

Depending on the results of the exams, the participants will be included in the -1 or 0 stages corresponding to the following:

  • Stage -1: Basics of Python or Fundamentals of Mathematics [2 Weeks]

An intermediate level people who know the basics of coding, including the python basics like data structures, operators or Numpy, will learn some algorithms on math like linear algebra and calculus for future stages.

People who are not that comfortable with coding but who has some intermediate skills in math will learn basics of Python like data structures and operators etc.

  • Stage 0: Python for Machine Learning [3 Weeks] 

In this stage both groups of mathematicians and coders will have an introduction to computational frameworks and plotting used in machine learning. It involves statistical learning and data visualization etc. After completing stage 0, all participants in the program will pass one more exam-interview and the best participants will continue the program till the end.

  • Stage 1: Machine Learning [12 Weeks]   

This stage will consist of 12 weeks of classes in the following format:

2 days of 2.5 hours of lecture + 5 hours of coding sessions over the weekends.

Topics will include:

Fundamentals of ML and Supervised Learning [5 weeks]

  1. Regression Models
  2. Classification Models
  3. Trees, Ensemble Methods

Unsupervised Learning [2 weeks]

  1. Clustering
  2. Dimensionality Reduction

Neural Networks [3 weeks]

  1. Neural Networks and Backpropagation
  2. Convolutional Neural Networks
  3. Recurrent Neural Networks

Statistical Machine Learning [2 weeks]

  1. Introduction to Graphical Models
  2. Hidden Markov ModelsPython for Machine Learning
  • Stage 2: Projects with Machine Learning [4 Weeks]

At this stage, the participants will be split to relevant groups and will have the opportunity to work with real projects on the basis of which will be provided data by private partners. The stage consists of 4 weeks team work and mentorship by specialists.

TRAINERS

  • ARSEN MAMIKONYAN
  • RAZMIK MELIKBEKYAN
  • VARDGES MAMBREYAN

The course will last 5 months and the price is 50 000 AMD per month. After finishing the final project you will get an opportunity to be hired by leading IT companies in Armenia.

For more information click "LINK TO ORIGINAL" below.

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