Machine Learning for Time-Series with Python

"Machine Learning for Time-Series with Python" is your comprehensive guide to mastering predictive analysis and anomaly detection for time-series data using Python. By exploring modern machine learning techniques and applying them to real-world datasets, you will be able to accurately forecast and model temporal patterns.

What this Book will help me do

• Gain in-depth knowledge of Python ecosystem tools for time-series analysis and forecasting.
• Learn to apply autoregressive, deep learning, and gradient boosting models to practical problems.
• Explore and visualize time-series data for detecting patterns and preparing features.
• Understand the theoretical grounding behind various time-series modeling approaches.
• Apply methods to real-world datasets from domains including finance, healthcare, and digital marketing.

Author(s)

Ben Auffarth is an experienced data scientist with deep expertise in machine learning, particularly for time-series data. With a strong academic background and extensive hands-on industry experience, Ben delivers insights that bridge theory and application. His writing focuses on making complex concepts approachable for practitioners.

Who is it for?

This book is for data analysts, data scientists, and Python developers eager to enhance their time-series analysis skills. It's suited for those with a Python programming background, looking to effectively apply machine learning to temporal data. Familiarity with statistical concepts will enrich your learning experience.