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Machine Learning Talks Coming to the ODSC Virtual Conference April 14-17 Machine Learning Talks Coming to the ODSC Virtual Conference April 14-17
Machine learning is one of the most fundamental, important, and highly-desirable skills for any data scientist. There’s so much that goes into becoming an... Machine Learning Talks Coming to the ODSC Virtual Conference April 14-17

Machine learning is one of the most fundamental, important, and highly-desirable skills for any data scientist. There’s so much that goes into becoming an ML pro, ranging from knowing the best frameworks to staying in-the-know with the latest libraries. To help any data scientist stay up-to-date with ML, here are a few highlighted talks coming to the ODSC East 2020 Virtual Conference that will cover a range of important topics.

[Related article: Learn Data Science Online at the ODSC East 2020 Virtual Conference]

Machine Learning Intro, Intermediate, and Advanced Talks: Andreas Mueller, PhD | Columbia Data Science Institute
Andreas Mueller will be presenting key ML skills, ranging from beginner to expert in a series of half-day training sessions.

Introduction to Machine Learning with scikit-learn
Intermediate Machine Learning with scikit-learn
Advanced Machine Learning: Pipelines and Evaluation Metrics
Advanced Machine Learning with scikit-learn: Imbalanced Classification and Text Data

Machine Learning in R: Jared Lander | Columbia Business School
Prefer to use R in data science? Jared will present two different workshops on how to use R for machine learning.

Machine Learning in R Part I: Penalized Regression and Boosted Trees
Machine Learning in R Part II: Using workflows to build an ML optimization pipeline

Target Leakage in Machine Learning: Yuriy Guts | DataRobot
This talk offers real-life examples of data leakage at different stages of data science projects, discusses countermeasures, and lays out best practices for model validation.

Machine Learning and Artificial Intelligence in 2020: Recent Trends, Technologies, and Challenges: Sebastian Raschka, PhD | University of Wisconsin-Madison
 In this talk, Sebastian will highlight the research and technology advances and trends of the last year(s), concerning GPU-accelerated machine learning and deep learning, and focusing on the most profound hardware and software paradigms that have enabled it.

Training and Operationalizing Interpretable Machine Learning Models: Francesca Lazzeri, PhD | Microsoft
In this talk, we will introduce some common challenges of machine learning model deployment and we will discuss multiple points in order to enable you to tackle some of those challenges.

Talks on ML Examples & Novel Use Cases: Dr. Kirk Borne | Booz Allen Hamilton

Adapting Machine Learning Algorithms to Novel Use Cases
Solving the Data Scientist’s Dilemma: the Cold-start Problem with 10+ Machine Learning Examples

Data Science and Machine Learning in the Cloud for Cloud Novices: Joy Payton | Children’s Hospital of Philadelphia
In this half-day hands-on training, we will use free-tier resources in the Google Cloud Platform (GCP) to introduce learners to the practical use of cloud computing resources in data science and machine learning.

Uplift Modeling Tutorial: Predictive and Prescriptive Analytics: Victor Lo, PhD | Fidelity Investments
This talk introduces the uplift concept, contrast with the traditional response modeling method, and reviews various predictive analytics approaches to Uplift Modeling. 

Missing Data in Supervised Machine Learning: Andras Zsom | Brown University, Center for Computation and Visualization
Andras will describe three advanced methods for handling missing data: multiple imputation, the reduced-feature (aka pattern submodel) approach, and XGBoost, which is one of the few machine learning algorithms that works with incomplete datasets. 

Validate and Monitor Your AI and Machine Learning Models: Olivier Blais | Moov AI
In this workshop, you will learn the best techniques that can be applied manually or automatically to validate and monitor statistical models.

Deciphering the Black Box: Latest Tools and Techniques for Interpretability: Rajiv Shah, PhD | DataRobot
The workshop will demonstrate interpretability techniques with notebooks, some in R and some in Python. Along the way, workshops will consider issues like spurious correlation, random effects, multicollinearity, reproducibility, and other issues.

This list doesn’t cover everything. Be sure to check out all of the ODSC East 2020 Virtual Conference speakers here and filter by the category of your choice. 

Alex Landa, ODSC

Alex Landa, ODSC

Alex drinks a lot of coffee and sometimes writes stuff. Connect with him on Twitter @alexandermlanda or LinkedIn: https://www.linkedin.com/in/alexander-landa-a0889857/

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