11 February 2017
450 Townsend St, San Francisco, CA 94107, Stati Uniti
Machine Learning with Python
Learn to use open source software like Python, Pandas, Numpy, Scikit-learn and Bokeh to build predictive models from a real-world data.
The workshop is meant to provide you with a solid base to build your machine learning skills. In particular, you will learn to:
- Recognize problems that can be solved with Machine Learning
- Select the right technique (is it a classification problem? a regression? needs preprocessing?)
- Load and manipulate data with Pandas
- Visualize and explore data with Matplotlib and Bokeh
- Build regression, classification and clustering models with Scikit-Learn
- Evaluate model performance with Scikit-Learn
- Build, train and serve a predictive model using Python, Flask and Heroku
The workshop is conceived to maximize the learning experience for everyone and includes 50% theory and 50% hands-on practice.
Are there any prerequisites?
Previous experience programming in Python or in other languages is advised to make best use of the workshop.
In the last 2 years Python has become a de-facto standard in data science and is widely adopted by most major companies. Reasons for this success include:
- large set of mature data science libraries => most needs covered
- worldwide community of enthusiasts => get help when you need it
- easy to learn, read and write => start contributing immediately
- supports both functional and object-oriented coding => versatile and powerful
- full stack programming language => easier interaction between data scientists and software engineers
The course is lead by Francesco Mosconi. Ph.D. in Physics and Data Scientist at Catalit LLC, he was formerly co-founder and Chief Data Officer at Spire, a YC-backed company that invented the first consumer wearable device capable of continuously tracking respiration and physical activity. Machine Learning and python expert he also served as Data Science lead instructor at General Assembly and at The Data incubator.
This course is part of a 3-weekends series covering machine learning and deep learning.
Check it out!