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.

Leave a Comment or Question

Help us make this conference the best it can be for you. Have questions you'd like this speaker to address? Suggestions for issues that deserve extra attention? Feedback that you'd like to share with the speaker and other attendees?

Join the conversation here (requires login)