For professionals ready to elevate their understanding of Python's immense time series ecosystem, Ben presents a comprehensive training course about machine learning for time series. 

Time Series Mastery

Immerse yourself in the world of time series forecasting with Ben's engrossing training course that is designed to guide you effortlessly from understanding the core principles of time-series data to expertly wielding a toolset of analysis approaches and modelling techniques. The course offers a unique blend of hands-on lessons and theoretical foundation that will help you imbibe machine learning principles that improve predictive systems by pairing problem definitions with suitable models, and teach you how to increase predictions' accuracy, and gain insights. This can be based on real-world case studies in operations management, digital marketing, finance, and healthcare sectors.

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This course, intended for data scientists or analysts with at least 1 year of experience with Python, employs a highly interactive format over three virtual mornings (via Zoom & Slack). Limited to a small group of approximately 10 persons, the focus is on honing skills from R&D to deployment via code reviews, and employing Notebook-backed models.

Prospective trainees:

Those desiring introductory to advanced solutions pertaining to modelling dynamic data alongside modelling methods enhancing predicting performance.

● Individuals eager to learn about adaptive models, reinforcement learning, deep learning methodologies, probabilistic models, and multivariate forecasting specifically in the context of time series forecasting.

Course Highlights:

● Introduction to dealing with time series data in Python and their analysis

● Theoretical and practical exposure to traditional approaches like moving averages and autoregressive models, as well as modern machine learning methods for time-series data

● Emphasis on adaptive models, reinforcement learning, probabilistic frameworks, deep learning, multivariate forecasting tailored for time-series prediction 

● Experience using common libraries such as Prophet, sktime, statsmodels, XGBoost, and TensorFlow for time series forecasting

● Techniques to select the best-suited model for specific time-series problems

What You'll Gain Post Course Completion:

● Profound knowledge on how to visualize, analyse and forecast time-series data

● New insights into constructing effective predictive models for time series data 

● Improved code and model quality within your team through practical guide-backed code discussions and collaborative sessions

Whether you are a seasoned professional or an aspiring newcomer in the field, this course is configured to upgrade your time-series forecasting skills, arming you with contemporary models and strategies. With a blend of theoretical instruction and hands-on exercises focusing on real-world case studies, this course provides learners with a complete toolkit to explore and analyse time series data confidently. You can anticipate an in-depth immersion into the world of time series, crystallized by practical experience and a network of fellow learners. Don't miss the opportunity to transform the way you understand and interact with time-series data.