Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK

Practical machine learning with Python

Charlotte Werger (ASI Data Science)
9:0012:30 Tuesday, 23 May 2017
Data science and advanced analytics
Location: Capital Suite 12
Level: Beginner
Average rating: ***..
(3.80, 5 ratings)

Who is this presentation for?

  • Developers, product managers, and software engineers

Prerequisite knowledge

  • Basic coding skills in any language

Materials or downloads needed in advance

  • A laptop with an up-to-date browser
  • Access to SherlockML platform. (Invitation code is being sent to registered attendees via email, otherwise, you'll be able to get the access code and register onsite).

What you'll learn

  • Gain an understanding of and experience implementing common machine-learning techniques
  • Understand practical considerations in applying machine learning to business problems
  • Learn a workflow that maximizes the success of a machine-learning project

Description

Python is a great language for getting started with machine learning, as it is equipped with a number of useful libraries for data analysis (e.g., pandas) and fast prototyping (e.g., scikit-learn). Python not only allows beginners to develop machine learning projects with ease but also offers a rich framework for advanced users, thanks to a passionate open source community and the availability of libraries such as Theano and TensorFlow.

Charlotte Werger offers a hands-on overview of implementing machine learning with Python, providing practical experience while covering the most commonly used libraries, including NumPy, pandas, and scikit-learn. Charlotte demonstrates the power and flexibility of these libraries through examples and challenges that are inspired by real-life projects. In addition to direct experience with Python coding, you’ll gain advice on machine-learning best practices (for example, managing the bias-variance trade-off and rigorously cross-validating statistical models) and learn how to structure an end-to-end data-science project. Along the way, you’ll catch a glimpse of more advanced topics in AI.

Photo of Charlotte Werger

Charlotte Werger

ASI Data Science

Charlotte Werger is the ASI education manager at ASI Data Science. A data scientist with a background in econometrics, Charlotte has worked in finance as a quantitative researcher and portfolio manager for BlackRock and Man AHL, using data science to predict movements in stock markets. She is a former ASI fellow, where she worked on predicting staff performance from psychometric test results, and has also worked on energy smart meter data analysis. Charlotte holds a PhD in economics from the European University Institute and an MPhil from Toulouse School of Economics.