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LIVE TRAINING: Introduction to PYTHON for Programming 

October 12th @12 PM EST

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  • 4 hour immersive session

  • Hands-on training with Q&A

  • Recording available on-demand

  • Certification of Completion

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Pricing: $147

Price: FREE with ODSC Bootcamp ticket or above

  • Purchase virtual or in-person Bootcamp ticket or above and attend Probabilistic Programming & Bayesian Inference with Python live training for free

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  • All the Bootcamp training sessions, workshops and talks at ODSC West (more than 200+ sessions)
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  • Probabilistic Programming & Bayesian Inference live training and recording
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  • Purchase Annual Premium Subscription and attend Probabilistic Programming & Bayesian Inference live training for $95

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  • Probabilistic Programming & Bayesian Inference  live training and recordings

Price: $147 (includes 30% discount)

  • Purchase your Probabilistic Programming and Bayesian Inference ticket by 2022/10/21 to redeem 30% discount

Includes:

  • Probabilistic Programming & Bayesian Inference with Python – 3 hours immersive session
  • Hands-on training with QA
  • Certification of Completion 

 

 

 

 

 

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Meet Your Instructor

Lara Kattan

Lara is a Data Science Manager at EY and occasional adjunct at the University of Chicago’s Booth School of Business, teaching Python and R. Previously she’s taught a data science bootcamp and built risk models for large financial institutions at McKinsey & Co.

Course Overview

What’s the plan? 

If you can write a model in sklearn, you can make the leap to Bayesian inference with PyMC3, a user-friendly intro to probabilistic programming (PP) in Python. PP just means building models where the building blocks are probability distributions! And we can use PP to do Bayesian inference easily. Bayesian inference allows us to solve problems that aren’t otherwise tractable with classical methods.

The intention is to get hands-on experience building PyMC3 models to demystify probabilistic programming / Bayesian inference for those more well versed in traditional ML, and, most importantly, to understand how these models can be relevant in our daily work as data scientists in business.

Let’s build up our knowledge of probabilistic programming and Bayesian inference! All you need to start is basic knowledge of linear regression; familiarity with running a model of any type in Python is helpful.

Learning Objectives

  • What probabilistic programming is and why it’s necessary for Bayesian inference

  • What Bayesian inference is, how it’s different from classical frequentist inference, and why it’s becoming so relevant for applied data science in the real world

  • How to write your own Bayesian models in the Python library PyMC3, including metrics for judging how well the model is performing

  • How to go about learning more about the topic of Bayesian inference and how to bring it to your current data science job.

Course Outline

What’s the plan? 

Matt Harrison has been working with Python and data since 2000. He has a computer science degree from Stanford. He has worked at many amazing companies, created cool products, wrote a couple books, and taught thousands Python and Data Science. Currently, he is working as a corporate trainer, author, and consultant through his company Metasnake, which provides consulting and teaches corporations how to be effective with Python and data science.

Course Overview

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Course Outline

Module 1:
What is probabilistic programming?

Module 2:
What is Bayesian inference and why should I add it to my toolbox on top of classical ML models?

Module 3:
What is PyMC3 and how can I start building and interpreting models using it?

  • PP is the idea that we can use computer code to build probability distributions
  • Theory of the primitives in probabilistic programming and how we can build models out of distributions
  • Classically, we had simulations, but they run in only one direction: get data input and move it according to assumptions of parameters and get a prediction
  • Bayesian inference adds another direction: use the data to go back and pick one of many possible parameters as the most likely to have created the data (posterior distributions)
  • Use Bayes’ theorem to find the most likely values of the model parameters
  • We’ll work through actual examples of models using PyMC3, including hierarchical models
  • Solving Bayes’ theorem in practice requires taking integrals, and if we don’t want to do integrals by hand, we need to use numerical solution methods
  • From the package authors: “[PyMC3 is an ]open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed”

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DATE

TIME:

DURATION:

LEVEL:

OCTOBER 13TH, 2022

1 PM EST, 10 AM PST

3 HOURS

BEGINNER

Prerequisites

  • Basic Python and machine learning, sklearn, some stats and probability

Upcoming Live Training

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