English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2h 51m | 1.10 GB
Time Series Analysis is one of the classic topics in data analysis and machine learning. It has significant business value and by the end of this course, you’ll understand why. The course teaches you the basics of time series analysis (e.g., relevant types of data analysis problems, classical approaches using autoregressive models, etc.); surveys recent advances (i.e., structured inference and deep learning-based approaches); reviews the best practices for handling time series data; and helps you understand the best use cases for time series analysis. The video focuses on state-of-the-art Python libraries and toolsets— the de facto standards for practical data science— that allow you to quickly move models from prototype to production. Learners should have basic familiarity with Python, data analysis, and data science.
- Discover the best practices in prediction and anomaly detection using Python
- Explore and understand the basics of time series analysis
- Survey recent advances in time series analysis and machine learning
- Learn how to use the most relevant Python data analysis libraries and toolsets
- Review key concepts related to time series storage and databases
- Understand the business value of analyzing time series data
Table of Contents
01 Introduction
02 What makes Time Series Analysis different from other types of data analysis
03 Classical Methods for Time Series Analysis
04 Python for Time Series Analysis
05 Storage of Time Series Data
06 Machine Learning Methods for Time Series Analysis
07 Advanced Methods for Time Series Analysis
08 Deep Learning for Time Series Analysis
09 Real world Time Series Analysis Use Cases
10 Best Practices and other Insights
11 Summary and Outlook
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