Machine learning with tabular & time series data.

Format:

Video

Contributor:

Massaron, Luca, instructor.

Tran, Khuyen, instructor.

Packt Publishing, publisher.

Language:

English

Subjects (All):

Machine learning.

Python (Computer program language).

Physical Description:

1 online resource (1 video file (01 hr., 07 min.)) : sound, color.

Edition:

[First edition].

Place of Publication:

[Birmingham, United Kingdom] : Packt Publishing, 2025.

Summary:

Machine Learning with Tabular & Time Series Data equips you with the skills to design, train, and deploy predictive models that work with structured and sequential data. With real-world examples and hands-on exercises, you'll learn how to clean and transform datasets, engineer features, and apply effective algorithms to uncover insights and drive decisions. Throughout, you'll gain experience applying anomaly detection, backtesting strategies, and model monitoring. By the end, you'll have a practical toolkit for building reliable ML solutions that can be applied in domains such as finance, supply chain, IoT, and retail. To access the supplementary materials, scroll down to the 'Resources' section above the 'Course Outline' and click 'Supplemental Content.' This will either initiate a download or redirect you to GitHub. What you will learn Preprocess and transform structured datasets in Python Engineer features to improve model accuracy and reliability Apply anomaly detection techniques to tabular/time series Backtest and evaluate models for robustness and reliability Monitor deployed models to maintain long-term performance Audience This course is designed for data scientists, analysts, and ML engineers who want to build predictive solutions using tabular and time series data. A basic understanding of Python and core ML concepts is recommended. Professionals in industries such as finance, retail, manufacturing, and IoT will especially benefit from the practical skills covered, which can be directly applied to forecasting, anomaly detection, and business-critical analytics. About the Authors Luca Massaron: Luca Massaron is a data scientist with over a decade of experience in transforming data into high impact, innovative artifacts, solving real world problems, and generating value for businesses and stakeholders. He is the author of numerous bestselling books on AI, machine learning, and algorithms. Luca is also a 3x Kaggle Grandmaster who reached number 7 in the worldwide user rankings for his performance in data science competitions. Additionally, he is recognized as a Google Developer Expert GDE in AI, Kaggle, and the cloud. Khuyen Tran: Khuyen Tran is a Developer Advocate at Nixtla. She wrote over 180 data science articles with over 100k views per month on Towards Data Science and published over 800 daily tips related to data science and Python at CodeCut. Her current mission is to make open source tools more accessible to the data science community and to educate data scientists on optimal engineering practices for data science projects.

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

1-80669-211-2

OCLC:

1550425806