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Open Class II - Machine Learning

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80 Bloor Street West, Suite 500

Toronto, ON M5S 2V1

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Event description



  1. 1. Introduction to Machine Learning and Predictive Modelling
  • o Machine Learning Industry Overview
  • o Career path
  • o Machine Learning problems
    • § Supervised vs Unsupervised
    • § Statistical learning
    • § Curse of dimensionality
    • § Variance-bias trade off
    • o Machine Learning Tools
      • § Pandas, Numpy, Scipy, Scikit-Learn, MLlib
      • § H2O, Amazon ML, Azure
      • § Theano, Tensoflow, Keras

Please make sure to install Anaconda follow the instruction below so that you can enjoy doing a mini project in class:

About the Course

Short Intro

In this course, students will work on real problems in classification and regression with supervised and unsupervised learning approaches. Students will explore sophisticated model evaluation approaches (cross-validation and bootstrapping) to make the models as generalizable as possible.

Potential Job Positions

  • Data Scientist
  • Research Analyst
  • Statistical Modeler
  • Predictive Modeler
  • Machine Learning Specialist
  • Statistical Data Analyst

Course Details

Who is this course for

  • This course is part of the skill-based data science track course series and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, recommender system, and natural language processing techniques to gain new insight into data. Only basic statistics background is expected, and the first course contains a refresh of these basic concepts. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in this course valuable in their studies and careers.


Python programming

Statistics and Linear Algebra

Ideally who have taken the "Python for Data Science" or equivalent courses

Strong curiosity and a passion for learning and applying machine learning technologies in real life

How is this course delivered

  • This classroom-based course is delivered with 40% lecture, 20% labs and 40% project
  • You will meet the instructors and teaching assistants in person and learn with your peer students
  • You will work on hands-on projects to aid you build your data science portfolio that is often the key to a successful job placement
  • You will work with your peer students in group on data challenges such as Kaggle
  • Use cases and best practice discussions will be delivered via Slack App

Assistance you will get from us

  • Our teaching assistants will help you with your questions throughout the learning period
  • One on one chat with our instructors and mentors
  • Resume help and suggestion upon completion of the course
  • Job referrals

Date and Time


80 Bloor Street West, Suite 500

Toronto, ON M5S 2V1

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