4.2 Read 5 reviews
Overview
Context
Time series data is ubiquitous. Whether it be stock market fluctuations, sensor data recording climate change, or activity in the brain, any signal that changes over time can be described as a time series.
Machine learning has emerged as a powerful method for leveraging complexity in data in order to generate predictions and insights into the problem one is trying to solve.
This Machine Learning for Time Series Data in Python course offered by Data Camp is an intersection between these two worlds of machine learning and time series data, and covers spectograms, and other advanced techniques in order to classify heartbeat sounds and predict stock prices.
Programme Structure
Chapters include:
- Time Series and Machine Learning Primer
- Predicting Time Series Data
- Time Series as Inputs to a Model
- Validating and Inspecting Time Series Models
Key information
Duration
- Part-time
- 1 days
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Campus Location
- New York City, United States
Disciplines
Machine Learning View 213 other Short Courses in Machine Learning in United StatesWhat students do after studying
Join for free or log in to access our complete career info list.
Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Prerequisites:
- Manipulating Time Series Data in Python
- Visualizing Time Series Data in Python
- Supervised Learning with scikit-learn
Tuition Fees
Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
-
International Applies to you
Applies to youNon-residentsFree - Out-of-StateFree
-
Domestic
Applies to youIn-StateFree
Additional Details
This course can be accessed for free with the Data Camp Premium or Teams subscriptions
Funding
Improve page content