When it comes to building a successful career in data science or AI, getting hands-on training to fill knowledge gaps or learn about the latest developments in the industry is always a good idea. At ODSC East this May 9th-11th, you’ll find hands-on training sessions in a wide range of subjects. Check out a few of them below.

Advanced Fraud Modeling & Anomaly Detection with Python & R part 1 and 2

Aric LaBarr, PhD | Associate Professor of Analytics | Institute for Advanced Analytics at NC State University

This course covers where data science can play a role in the fraud framework at an organization as well as lay out how to build an analytically advanced fraud system. It also explores statistical and machine learning approaches to anomaly detection. These approaches can also be used in other industries to help find unique customers or problems that exist.

Deep Learning with PyTorch and TensorFlow part 1 and 2

Dr. Jon Krohn | Chief Data Scientist | Nebula.io

This introduction to Deep Learning brings high-level theory to life with interactive examples featuring PyTorch, TensorFlow 2, and Keras.Paired with hands-on code demos in Jupyter notebooks as well as strategic advice for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of artificial neural networks to train Deep Learning models following all of the latest best practices.

Beyond the Basics: Data Visualization in Python

Stefanie Molin | Software Engineer, Data Scientist, Chief Information Security Office | Bloomberg LP | Author of Hands-On Data Analysis with Pandas

In addition to exploring the Matplotlib library, this session will introduce interactive visualizations using HoloViz, which provides a higher-level plotting API capable of using Matplotlib and Bokeh (a Python library for generating interactive, JavaScript-powered visualizations) under the hood.Python library for generating interactive, JavaScript-powered visualizations) under the hood.

Introduction to scikit-learn: Machine Learning in Python 

Thomas J. Fan | Staff Software Engineer | Quansight Labs

This session will start with an overview of scikit-learn’s API for supervised machine learning, with a focus on its three methods: fit to build models, predict to make predictions from models, and transform to modify data. You will cover the importance of splitting your data into train and test sets for model evaluation, combining preprocessing techniques with machine learning models using scikit-learn’s Pipeline, and the Pandas output API recently introduced in version 1.2. 

Intermediate Machine Learning with scikit-learn: Pandas Interoperability, Categorical Data, Parameter Tuning, and Model Evaluation

Thomas J. Fan | Staff Software Engineer | Quansight Labs

In this session, you’ll learn about scikit-learn, a Python machine-learning library used by data science practitioners from many disciplines. Topics covered include Pandas interoperability, categorical data, parameter tuning, and model evaluation. As part of this session you will use all the ML techniques you learned to train and evaluate a model on a house pricing dataset with Histogram-based Gradient Boosted Trees.

NLP Fundamentals

Leonardo De Marchi | VP of Labs | Thomson Reuters

This course will take you through NLP fundamentals, including pre-processing techniques,tf-idf, embeddings, and more, and will incorporate practical coding examples, in python, to teach how to apply the theory to real use cases. The goal of this workshop is to provide you with all the basic tools and knowledge you need to solve real problems and understand the most recent and advanced NLP topics. 

Creative AI

Leonardo De Marchi | VP of Labs | Thomson Reuters

In this workshop, you’ll explore how artificial intelligence can be used to generate creative outputs and to inspire technical audiences to use their skills in new and creative ways. It will include a series of code exercises designed to give participants hands-on experience working with AI models to generate creative outputs. Participants should have a basic understanding of machine learning concepts and be comfortable coding in Python. 

Advanced Gradient Boosting (I): Fundamentals, Interpretability, and Categorical Structure and Advanced Gradient Boosting (II): Calibration, Probabilistic Regression, and Conformal Prediction

Brian Lucena | Principal | Numeristical

Learn about Gradient Boosting, the most effective method for classification and regression problems on tabular data. Over the course of two sessions, you’ll learn about best practices for model building and hyper-parameter tuning, interpreting the model, exploiting categorical structure, calibrating the probabilities of classification models, Probabilistic Regression, and tools for Conformal Prediction–a hot topic that can provide prediction intervals with strong theoretical guarantees.

Getting Started with Hyperparameter Optimisation

Nikolay Manchev, PhD | Head of Data Science for EMEA | Domino Data Lab

Join this workshop for a hands-on exploration of various techniques for optimizing hyperparameters, including  Grid search, Random search, Bayesian optimization, and Evolutionary algorithms. You will discuss both the theory behind each approach and the various pros and cons.

Register today

Remember, that’s just the tip of the iceberg. Check out our preliminary schedule for more sessions and speakers and register soon to save 40% on any in-person or virtual ODSC East pass.