Course: Fundamentals of AI and ML

Artificial intelligence (AI) provides cutting-edge tools to help organizations predict behaviors, identify key patterns, and drive decision-making in a world that is increasingly made up of data. In this course, you will explore the full definition of AI, how it works, and when it can be used. You will identify the types of data tools and technologies AI uses to operate. Next, you will discover a framework for using the AI life cycle and data science process. Finally, you'll consider what you need to keep in mind as you implement AI techniques in your organization. Upon completion of this course, you'll be familiar with common concepts and use cases of artificial intelligence (AI) and be able to outline strategies for each part of the AI life cycle.

Data science methods are used across several industries to deliver value to businesses. Machine learning (ML) is a data science method that uses prediction algorithms that find patterns in massive amounts of data, allowing machines to predict future results and make decisions with minimal human intervention. Through this course, learn foundational methods for using machine learning. Examine what machine learning is, how it is categorized, and common machine learning challenges. Next, learn about common types of machine learning tasks, such as clustering, classification, and regression. Finally, explore the types of regression, including simple and multiple linear regression. Upon completion, you'll be able to define machine learning and methods for using it.

In data science, many statistical and analytical techniques can be used to pull meaningful insights from data. Additionally, some advanced data science methods rely on other foundation data science methods, such as the case of text mining. Through this course, learn about advanced data science methods and their use cases. Explore advanced machine learning (ML) methods such as text mining and graph analysis and their uses. Next, learn about the anomaly and novelty detection processes. Finally, examine association rule mining and neural networks and their use cases across industries. After course completion, you'll be able to outline advanced methods for data science.

Understanding model evaluation is crucial for making reliable, accurate, and ethical decisions when using artificial intelligence (AI) and machine learning (ML) in practical scenarios. In this course, you'll explore AI/ML model evaluation and interpretability in-depth, gaining a strong grasp of these essential components to make AI/ML work effectively for your organization. This course focuses on the key concepts and metrics needed to assess how well models perform. Understanding model evaluation is crucial for making reliable, accurate, and ethical decisions when using AI/ML in practical scenarios. Upon completing this course, you will be well-prepared to make informed decisions and maximize the potential of AI/ML within your organization.