The final schedule for ODSC West 2022 has been released and we have the information for all of our training sessions. There are too many to mention here, but take a look at the sneak peeks provided below for the event November 1st-3rd.
Machine Learning with Python: A Hands-On Introduction: Clinton Brownley, PhD | Data Scientist | Meta
In this hands-on workshop, you’ll have the opportunity to learn how to fit, tune, and evaluate a variety of parametric and non-parametric models for the purpose of classification and regression. You will have the chance to practice how to build models and make predictions with them in Python.
Beyond the Basics: Data Visualization in Python: Stefanie Molin | Data Scientist, Software Engineer | Bloomberg | Author of Hands-On Data Analysis with Pandas
Join this session to learn the skills to build impactful, inviting, customized visualizations for your data using Python. You’ll also be introduced to HoloViz, which provides a higher-level plotting API capable of using Matplotlib and Bokeh under the hood.”
Anomaly Detection with Python and R: Aric LaBarr, PhD | Associate Professor of Analytics | Institute for Advanced Analytics at NC State University
To ensure the integrity of your analysis and decision-making process, it’s essential to monitor for and detect anomalies in your data. During this session, you’ll explore anomaly detection through the lens of fraud, as well as examine some ML-based techniques that are applicable to almost any industry.
Image Recognition with OpenCV and TensorFlow: Oliver Zeigermann | Head of Artificial Intelligence | OPEN KNOWLEDGE GmbH
During this session, you will explore both classic image processing using OpenCV and machine learning pattern matching using TensorFlow’s Keras API. Through hands-on exercises, you’ll learn how to extract objects and remove noise with OpenCV and then how to use those objects to generalize unseen objects with TensorFlow.
Democratizing Distributed Compute and Machine Learning: A Tour of Three Frameworks: Adam Breindel | Independent Consultant
During this session, you’ll examine the strengths, weaknesses, and patterns of three open-source tools, Apache Spark, Ray, and Dask, for data analysis. Through hands-on coding, you’ll get a sense of how each of these tools work, their advantages and disadvantages, and which one might be right for your organization. hard, what life is like with these tools, and which ones may be right for your organization.
Building a Semantic Search Engine: Nidhin Pattaniyil | Machine Learning Engineer | Walmart Global Tech and Vishal Rathi | Senior Software Engineer | Walmart Global Tech
During this session, you’ll explore how state-of-the-art techniques are using embedding for retrieval, instead of tf-idf and BM25. Over the course of 4 sections, you’ll learn how to create a production information retrieval system that relies on embeddings and vector similarity.
StructureBoost: Gradient Boosting with Categorical Structure: Brian Lucena | Principal | Numeristical
In this session, you will be covering Gradient Boosting, which remains the most effective method for classification and regression problems on tabular data. You’ll discuss best practices for building Gradient Boosting models, parameter tuning, model interpretability, probabilistic regression, and categorical structure.
NLP Fundamentals: Leonardo De Marchi | VP of Labs | Thomson Reuters and Laura Skylaki, PhD | Manager of Applied Research | Thomson Reuters Labs
Join this session to build an understanding of the fundamentals of NLP from Leonardo De Marchi, an acclaimed data science instructor, practitioner, and entrepreneur. In addition to his long and varied career working in the professional sports, social media, and education industries, Leonardo De Marchi, is also the author of Hands-On Deep Learning.
Deep Learning with Python and Keras (TensorFlow 2): Amita Kapoor, PhD | Artificial Intelligence and Data Analytics Expert, Educator, Author, Expert in Remote Team Management | Neuromatch Academy
This session is designed to give you a comprehensive look at the Keras API and the TensorFlow 2.0 ecosystem. At the end of this training, you’ll have an understanding of TensorFlow 2.0 features such as tf.Datasets, Autograph, eager computation, and more.
Getting Started with Hyperparameter Optimisation: Nikolay Manchev, PhD | Head of Data Science for EMEA | Domino Data Lab
In this workshop, you’ll tackle one of the toughest challenges in machine learning: hyperparameter optimization. Over the course of this session you will go over a variety of techniques for optimizing hyperparameters: from Grid search to Evolutionary algorithms.
Introduction to scikit-learn: Machine Learning in Python: Corey Wade | Founder, Director | Berkeley Coding Academy | Author
Join this session to become well-versed in the entire scikit-learn suite to fit models, score models, make predictions from models, and fine-tune models. By the end of this workshop, you should be able to confidently build, score, fine-tune, and make predictions from Machine Learning models in scikit-learn.
Practical Adversarial Learning: How to Evaluate, Test, and Build Better Models: Dr. Blaine Nelson | Principal Machine Learning Engineer | Robust Intelligence
Although machine learning has evolved considerably, there are still many challenges that can cause a model to be vulnerable to mistakes and malfeasance. This session will cover the techniques used to construct adversarial examples, tools for identifying vulnerability, and procedures for producing better models.
Register for ODSC West now
We are just a few days away from the event and in-person tickets are selling out, so be sure to get yours soon!