AI for Time Series Analysis and Forecasting (with TensorFlow and Keras)

Course Objectives

Understanding Artificial Intelligence / AI Overview

• A brief history of AI
• Types of AI systems
• Training machine learning models
• Applying models for prediction

Time series processing and forecasting elements

• Traditional Time Series forecasting with ARIMA models
• Defining Autocorrelation
• Understanding the Dickey-Fuller Test

Forecasting with TensorFlow and Keras

• Google democratization of AI with TensorFlow
• Types of neural network (Perceptron, CNN, LSTM) and their use
• Forecasting with TensorFlow
• Using RNN and LSTM in time series prediction.
• Validation and metrics of Time Series Prediction models

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate.  We offer additional data science, analytics, AI / machine learning / depp learning, programming and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

This course is geared for experienced data analysts, developers, engineers or anyone tasked with utilizing AI with Time Series Analysis with Python.  Attending students are required to have

• A background in basic Python development skills.
• Basic to Intermediate IT Skills Deep Learning for IoT.
• Good foundational mathematics or logic skill.
• Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.

Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

Understanding Artificial Intelligence / AI Overview

• A brief history of AI
• Types of AI systems
• Training machine learning models
• Applying models for prediction

Time series processing and forecasting elements

• Traditional Time Series forecasting with ARIMA models
• Defining Autocorrelation
• Understanding the Dickey-Fuller Test

Forecasting with TensorFlow and Keras

• Google democratization of AI with TensorFlow
• Types of neural network (Perceptron, CNN, LSTM) and their use
• Forecasting with TensorFlow
• Using RNN and LSTM in time series prediction.
• Validation and metrics of Time Series Prediction models

Course Materials

Each student will receive a Student Guide with course notes, code samples, software tutorials, step-by-step written lab instructions, diagrams and related reference materials and links (as applicable). Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.

Lab Setup Made Simple.   All course labs and solutions, data sets, Tableau course software (limited version, for course use only), detailed courseware, lab guides and resources (as applicable) are provided for attendees in our easy access, no installation required, remote lab environment for the duration of the course. Our tech team will help set up, test and verify lab access for each attendee prior to the course start date, ensuring a smooth start to class and successful hands-on course experience for all participants.