Machine Learning 1



Photo Deep Learning 1 course - 2018


You'll be able to :

  • Apply simple machine learning techniques in your area of expertise;
  • Understand the potential and the limitations of some of the most used machine learning algorithms;
  • Use machine learning packages and libraries, understanding the fundamental mechanisms behind them.

  • Requirements :

  • Beginner programming skills in Python, or in another object-oriented language;
  • Possess a general quantitative proficiency and understand the basics of linear algebra;
  • Laptop with minimal computing power: RAM>2GB, CPU clock>1.2GHz.

  • Course features

    Lecturer     Valerio Rossetti
    Starting     18/March/2019
    Ending     04/May/2019
    Training Sessions     Monday and Wednesday, 7pm-9pm
    Full-Immersion Saturdays     13 April and 04 May, 10am-7pm
    Location      Coworking Voisins , Geneva
    # hours     40
    Max Students     15
    Language     English
    Full Price     1600 CHF

    Description

    With this course, you will make your first steps into the world of machine learning. This course will gradually bring you from simple analytical techniques (Linear and Logistic Regression) to more complex and powerful algorithms such as Artificial Neural Networks. A large fraction of the course is devoted to hands-on sessions in which you will apply theoretical concepts to simple but realistic problems.

    The course includes two full-immersion Saturday of 8 hours each. These sessions will be dedicated to more complex projects in various sectors such as banking, insurance, engineering or marketing.

    This course is tailored to professionals working in the private industry, who want to enrich their quantitative skills with very hot and powerful tools. It can also be suitable for academic researchers, who are looking for career opportunities in the private sector.

    How it works
    How to works
    Registration
    You can pay immediately if you think you have the right skills for the course. You will receive a confirmation message. If you are not sure, you can ask for an assessment. We will meet you and discuss how you fit the requirements.
    How to works
    Connect with amazing peers
    During the course, you will interact with professionals coming from different sectors of the professional world and academia. Coding together and making small hackathons is the best way to learn.
    How to works
    Certificate
    Your progress will be monitored throughout the course and you will present one of your projects to experts in the sector. In case of positive results, you will receive a certificate of completion that shows that you have mastered the subjects of the course and that you are able to apply machine learning techniques to a simple real-world problem.

    Photo Deep Learning 1 course - 2018




    Content
    FAQs
    Yes, you should. Machine learning and data analytics are changing the way things are done in the professional world. This course gives you a unique opportunity to understand this change and start taking part in it. By the end of the course, you will be able to build simple machine learning solutions in your area of expertise, thus making your first steps into the world of advanced analytics.
    We, typically, do not accept people only for a few modules, as we think that a full grasp of the course is necessary to be able to understand the techniques to apply in the real world. Moreover, by only taking up a selective number of modules, we will not be able to give you the certificate at the end.
    You will need a laptop with Windows, MacOS, or Linux installed. The minimum requirements for the hardware are RAM>2Gb and CPU clock>1.2GHz. Please contact us if you have any doubts about your hardware.
    This course is an excellent opportunity to understand what people exactly mean by machine learning and how it is applied in fields such as banking, insurance, marketing, and engineering. If you are interested in such topics and in advanced analytics, then please check out the enrollment prerequisites.

    Photo Deep Learning 1 course - 2018