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.
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.
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.
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.
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:
In this liveProject series, you’ll tackle different areas of forecasting and model building. The skills you learn are the same kind used to solve complex problems by forecasters and data scientists in the industry.
geekle is based on a wordle clone.