Introduction: This workshop will provide a hands-on introduction to Machine Learning (ML) with an overview of Deep Learning (DL).
Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
Specific: Can you think of what an answer to your question would look like? The more clearly you can see it, the more specific the question is.
Measurable: Is the answer something you can quantify? It’s hard to make decisions based off things that aren’t in a really data-driven way.
Actionable: If you had the answer to your question, could you do something useful with it? If not, you don’t necessarily have a bad question but you may not want to expend a lot of resources answering it.
Realistic: Can you get an answer to your question with the data you have? If not, can you get the data that would get you an answer?
Timely: Can you get an answer in a reasonable time frame, or at least as before you need it? This is usually not a big issue, but if you operate according to a tight schedule, you may need to think about it.