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Deep Learning for Business Made Simple

Published by O'Reilly Media, Inc.

Intermediate content levelIntermediate

Leverage ChatGPT to accelerate coding, debug model architectures, and unlock insights

What you’ll learn and how you can apply it

  • Develop and train deep learning models using TensorFlow and Keras to solve business-relevant problems
  • Apply preprocessing and feature engineering techniques to prepare structured and unstructured business data for deep learning workflows
  • Leverage generative AI tools to accelerate coding, debug model architectures, and interpret deep learning outputs more effectively
  • Evaluate deep learning model performance and communicate results using business-friendly metrics and visual storytelling techniques
  • Translate complex model findings into actionable insights that inform strategy, marketing, and operational decision-making

Course description

Deep learning powers the systems that drive image recognition, natural language understanding, and recommender engines. Yet for many business professionals and analysts, deep learning remains an intimidating field. But with intuitive analogies and hands-on experience, Chester Ismay demystifies neural networks, guiding you not only to build models but also to interpret and communicate their impact in a business context.

You’ll use TensorFlow and Keras to explore deep learning through real-world business examples such as classifying product images, analyzing customer feedback, and forecasting emerging trends. You’ll learn how generative AI tools like ChatGPT support coding, experimentation, and comprehension. You’ll practice prompt engineering techniques to clarify TensorFlow syntax, troubleshoot errors, and generate visual and narrative summaries of model outputs for stakeholders. Whether you’re a business analyst, data professional, or manager eager to bring AI capability to your team, this course will help you confidently apply deep learning in ways that drive real business value.

This live event is for you because...

  • You’re a business analyst, data analyst, or data-savvy professional looking to expand your analytical toolkit with practical deep learning skills.
  • You want to understand how neural networks can solve real-world business challenges, from customer insight generation to demand forecasting.
  • You’re a manager or decision maker interested in learning how deep learning models create business value and how to communicate their impact effectively.
  • You’re a Python user who has worked with data and machine learning basics, and you’re ready to explore how deep learning frameworks like TensorFlow and Keras fit into modern analytics workflows.
  • You’re curious about how generative AI tools like ChatGPT can enhance your ability to code, experiment, and interpret model results.

Prerequisites

  • Basic familiarity with Python programming, including variables, functions, and libraries such as pandas or NumPy
  • Some experience with machine learning fundamentals (e.g., supervised learning, model evaluation) is recommended but not required
  • Comfort working with Jupyter notebooks or a similar Python coding environment
  • No advanced math background is required

Recommended preparation:

Recommended follow-up:

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Deep learning in the modern business landscape (15 minutes)

  • Presentation: What deep learning is and why it matters for business; key business applications—customer insight extraction, product recommendation, trend forecasting, and automation
  • Hands-on exercise: Set up the Python environment with TensorFlow, Keras, and Jupyter
  • Q&A

Demystifying neural networks for business insight (50 minutes)

  • Presentation: Explaining neural networks through business analogies; layers as “teams of specialists” and training as iterative improvement cycles; understanding model components (layers, activation functions, loss, and optimization)
  • Hands-on exercises: Build a simple feedforward neural network in TensorFlow; use ChatGPT to interpret TensorFlow errors and clarify model behavior
  • Q&A
  • Break

Deep learning for images and customer experience (55 minutes)

  • Presentation: Convolutional neural networks (CNNs) and business applications; use cases (product image classification, defect detection, visual brand monitoring; transfer learning and resource efficiency)
  • Hands-on exercises: Classify product images using a pretrained CNN in Keras; use generative AI to explain model layers and suggest architecture improvements
  • Q&A
  • Break

Deep learning for text and customer sentiment (55 minutes)

  • Presentation: Natural language processing (NLP) with deep learning; understanding customer sentiment, service logs, and brand feedback; comparing traditional analytics versus deep learning for text interpretation
  • Hands-on exercise: Build a sentiment analysis model with embeddings in TensorFlow/Keras; use ChatGPT to summarize sentiment analysis results into stakeholder-ready narratives
  • Q&A
  • Break

Interpreting and communicating deep learning results (50 minutes)

  • Presentation: Evaluating model performance in a business context; accuracy, precision, recall, and business cost-benefit trade-offs; communicating “how the model thinks” using examples, visuals, and AI-assisted summaries
  • Hands-on exercise: Interpret model predictions and visualize performance using confusion matrices and charts; use generative AI to generate executive summaries of model outcomes
  • Q&A

Wrap-up and Q&A (15 minutes)

Your Instructor

  • Chester Ismay

    Dr. Chester Ismay is an experienced data science educator and consultant. Chester enjoys helping others get into data science, figuring out how to best practice and improve their skills. He is co-author of "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at https://moderndive.com/v2/ He likes leading education and data science teams to improve best practices based on data from the learning sciences. Throughout his career, he has worked in academia, as a corporate trainer, at tech bootcamps, and as an independent consultant in the fields of education, insurance, and sports analytics.

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Skill covered

Deep Learning