Course
Machine Learning for Time Series Data in Python
AdvancedSkill Level
Updated 02/2026PythonMachine Learning4 hr13 videos53 Exercises4,550 XP52,837Statement of Accomplishment
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Prerequisites
Manipulating Time Series Data in PythonVisualizing Time Series Data in PythonSupervised Learning with scikit-learn1
Time Series and Machine Learning Primer
This chapter is an introduction to the basics of machine learning, time series data, and the intersection between the two.
2
Time Series as Inputs to a Model
The easiest way to incorporate time series into your machine learning pipeline is to use them as features in a model. This chapter covers common features that are extracted from time series in order to do machine learning.
3
Predicting Time Series Data
If you want to predict patterns from data over time, there are special considerations to take in how you choose and construct your model. This chapter covers how to gain insights into the data before fitting your model, as well as best-practices in using predictive modeling for time series data.
4
Validating and Inspecting Time Series Models
Once you've got a model for predicting time series data, you need to decide if it's a good or a bad model. This chapter coves the basics of generating predictions with models in order to validate them against "test" data.
Machine Learning for Time Series Data in Python
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FAQs
What machine learning tasks are applied to time series data in this course?
You will classify heartbeat sounds and predict stock prices by extracting features from time series data and building models with scikit-learn.
What prerequisites should I have before starting this advanced course?
You need experience with pandas, matplotlib, supervised learning with scikit-learn, and prior courses on manipulating and visualizing time series data in Python.
Does the course cover feature engineering for time series?
Yes. Chapter 2 focuses entirely on extracting common features from time series data, including spectrograms and other techniques to use as model inputs.
How does the course handle validation for time series models?
Chapter 4 covers best practices for generating predictions and validating time series models against test data, addressing the unique challenges of temporal data.
What makes this course different from a general machine learning course?
It focuses specifically on the intersection of ML and temporal data, covering time-series-specific feature engineering, prediction patterns, and validation considerations that standard ML courses skip.
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