Machine Learning 102: Time Series & Forecasting

Description

This course introduces time series analysis and forecasting techniques using Python. Participants will explore temporal data handling, apply basic and machine learning-based forecasting methods, and evaluate model performance using real-world datasets and the sktime library.

Indicative Duration: 8 training hours
*Duration is adjusted based on the final scope and the target audience.

Scope

1. Understanding Time Series Fundamentals
  • Introduction to Time Series
  • Representing temporal data
  • Theory and Basic concepts
  • Terminology
2. Intro to Forecasting
  • Use of datetime in python
  • Interpolation
  • Intro to forecasting
3. Basic Forecasting Techniques and Evaluation
  • Naive forecasting methods
  • Evaluating a forecasting model’s performance
4. Advanced Forecasting with Machine Learning
  • Training a ML model for forecasting
  • Sktime library

Learning Objectives

Upon completion of the course participants will be able to:

  1. Understand the fundamental concepts and terminology of time series data.
  2. Manipulate and represent temporal data using Python.
  3. Apply basic forecasting techniques and evaluate their performance.
  4. Use interpolation and datetime features for time-based data preparation.
  5. Train machine learning models for forecasting using the sktime library.

Target Audience

  • Roles: Data Scientists, ML Engineers, Business Analysts, Forecasting Specialists
  • Seniority Levels: Intermediate professionals with prior ML experience looking to specialize in time series forecasting

Prerequisite Knowledge

  • Basic understanding of machine learning principles
  • Experience in Python programming
  • Familiarity with pandas and data preprocessing is a plus
  • Completion of Machine Learning 101 or equivalent experience

Delivery Method

Sessions can be delivered via the following formats:

  • Live Online – Interactive virtual sessions via video conferencing
  • On-Site – At your organization’s premises
  • In-Person – At Code.Hub’s training center
  • Hybrid – A combination of online and in-person sessions

 

The training methodology combines presentations, live demonstrations, hands-on exercises and interactive discussions to ensure participants actively practice AI in realistic work scenarios.

Tags:

Date

On Demand

Organizer

Code.Hub
Email
[email protected]