Three-Project Series

End-to-End Time Series Forecasting with Deep Learning you own this product

prerequisites
intermediate Python • basic SQL • intermediate data science • basics of deep learning • basics of data visualization
skills learned
build time series forecasting models • enhance the models with deep learning • serve predictions with a REST API using FastAPI
Jiahao Weng
3 weeks · 6-8 hours per week average · INTERMEDIATE

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team

5, 10 or 20 seats+ for your team - learn more


In this liveProject series, you’ll learn how to deliver an end-to-end machine learning application for time series forecasting. Taking on the role of a data scientist at a huge retailer, you’ll build time series forecasting models to anticipate the future so your bosses can make better decisions. You’ll go all the way through from creating your baseline testing models, to enhancing with deep learning, to even deploying your model as an easy-to-use usable application. The skills you learn are perfect for solving some of the most complex problems of data science, and are in high demand across the industry.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

here's what's included

Project 1 Prepare Time Series Data

In this liveProject, you’ll create baseline models with Naive and sNaive methods for time series forecasts that you can use as a point of comparison for other models. Taking on the role of a data scientist for a large retail company, you’ll go hands-on to prepare sales data, create the baseline models, and optimize a Prophet model to compare against your baseline.

Project 2 Forecast using Deep Learning

In this liveProject, you’ll use deep learning to implement powerful time series forecasting models that can beat the performances of previous models. You’ll work with the Python package “PyTorch Forecasting” and the deep learning models LSTM and N-BEATS. You’ll also get experience with key techniques of cross learning, ensembling, and hyperparameter tuning.

Project 3 Deploy a Forecasting Model

In this liveProject, you’ll architect a solution to serve predictions from time series forecasting models over a REST API. Once you’ve architected your solution from a high-level perspective, you’ll monitor and assess the performance of the model and potentially undertake retraining to improve accuracy.

book resources

When you start each of the projects in this series, you'll get full access to the following book for 90 days.

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project author

Jiahao Weng
Jiahao Weng is a machine learning practitioner and a senior data scientist at a multinational company where he delivers projects ranging from proof-of-concept to production machine learning systems. As a freelance writer, he also contributes data science Medium articles to share his knowledge with the community.

Prerequisites

This liveProject series is for intermediate data scientists interested in tackling their first end-to-end deep learning project. To begin this liveProject, you will need to be familiar with the following:


TOOLS
  • Intermediate Python
  • Basic Google Colab
  • Basic Jupyter Notebook
  • Basic SQL
TECHNIQUES
  • Intermediate data science
  • Basics of data visualization
  • Basics of deep learning

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.