In this module, we will introduce the course structure and objectives, providing an overview of the key content and what you can expect. We will also highlight a special offer available to learners, outlining how it enhances the learning experience. This section ensures you are equipped with all the necessary details before diving into the course material.

In this module, we will guide you through the optional warm-up exercise to get familiar with the course environment. You will also learn where to access and download the code needed for the course, ensuring you have everything in place to start coding. This section is essential for setting up your workspace for a smooth learning experience.

In this module, we will introduce the foundational concepts of time series analysis, explaining what it is and how it’s used. We will also explore the distinction between modeling and predicting, and cover essential transformations to improve your data. Finally, you will gain insights into enhancing your analysis with feedback and suggestions.

In this module, we will cover the core principles of financial time series, providing you with a solid foundation. You’ll learn about random walks and the Random Walk Hypothesis, which play a critical role in financial modeling. Additionally, we will explore the concept of naive forecasting and why establishing baselines is essential for accurate predictions in finance.

In this module, we will dive deep into the ARIMA model, exploring its components like AR(p) and MA(q), and understanding how to apply it for time series forecasting. You will also learn to identify stationarity, compute ACF and PACF, and use Auto ARIMA for model selection. We will provide hands-on coding examples for various data types, allowing you to practice forecasting with ARIMA in real-world scenarios.

In this module, we will guide you through the process of setting up your development environment. You'll first perform a pre-installation check to ensure everything is in place, then set up Anaconda to manage your dependencies. Finally, we will show you how to install key libraries needed for the course, including Numpy, Scipy, and TensorFlow, so you can start working on hands-on projects right away.

In this module, we will provide extra support for beginners by covering the basics of coding and how to become more confident in writing your own code. You will learn how to effectively use Jupyter Notebook, with a demonstration of its advantages. Additionally, we will introduce you to GitHub and offer optional coding tips to enhance your learning and project management.

In this module, we will share strategies to maximize your success in this course, offering insights into the best learning approaches based on your experience level. You will also assess the course’s suitability for your background and determine whether to follow an academic or practical path. Finally, we’ll guide you on the best order to take related courses to enhance your machine learning journey.