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July 14, 2021


9:00 AM – 4:00 PM

Featured Workshops -

  • Introduction to Machine Learning with Scikit-learn

  • Introduction to Neural Networks

  • Natural Language Processing in Python with spaCy 3.0

Workshop Sessions

Introduction to Machine Learning with Scikit-learn

Scitkit-learn is a popular open source machine learning library that supports supervised and unsupervised learning. Additionally, it provides various utilities for data preprocessing, model fitting, selection, evaluation, and many other tasks. This workshop will help you to gain an understanding of the basics of machine learning and data preprocessing, and will serve as a practical guide to applying the power of scikit-learn to implement traditional machine learning algorithms. We will also touch on the topic of data pipelines and learn how they can be used to build consistent data flows for machine learning projects.


This workshop is meant for beginner data scientists with at least some programming experience with the Python programming language.

Experience Level: Beginner


Required Prior Skills: Some programming experience with Python

Introduction to Neural Networks

In this hands-on workshop, I will provide a brief introduction to Neural Networks, including the fundamental concepts and terminology, which will be followed by coding exercises.

Experience Level: Intermediate


Required Prior Skills: No prior knowledge of Neural Networks or TensorFlow is necessary. However, familiarity with classification models and Python will be helpful.

The agenda is as follows:


1. Introduction to Multilayer Perceptron (MLP)
2. Develop hand-written digits recognition model
3. Introduction to Convolutional Neural Networks (CNN)
4. Develop an image classification model using CNN
5. Image tagging


All coding exercises will be done in Jupyter Notebooks using Python. We will use TensorFlow package, so please install TensorFlow 2 prior to attending the workshop. Attendees will have access to two sets of Jupyter Notebooks: tutorial and complete. Attendees who prefer to type code along with me (within partially-coded Jupyter Notebooks) can use the tutorial version, and others may choose to directly execute code cells in complete Jupyter Notebooks.

Natural Language Processing in Python with spaCy 3.0

In this session, we’ll survey a variety of natural language processing techniques in Python, demonstrated through plenty of examples with working code and real-world data. We’ll pay special attention to spaCy 3.0 — a recent major release of one of the core libraries for natural language processing in Python. In version 3.0, spaCy introduces new tools and workflows to make it easier to develop, ship, and run custom-built natural language processing projects and products.

Experience Level: Intermediate


Required Prior Skills: Intermediate Python

Webinar Speakers

Vishal Patel

ManaFounder & Chief Data Scientistger

Derive, LLC

Katia Mastropas

Data Science Team Lead

James River Group

Patrick Harrison

Data Scientist


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