Statistics and Data Analysis with Excel, Part 1

This is an introductory microlearning course that aims to define Generative AI, how it is used, and how it differs from conventional machine learning methods. The course also covers Google Tools that can help you develop your own Generative AI applications.

There are 4 modules in this course AI isn't just for engineers. If you want your organization to become better at using AI, this is a course you should encourage everyone to take—especially your non-technical colleagues. In this course, you will learn: - The meaning of common AI terminology, including neural networks, machine learning, deep learning, and data science - What AI can—and cannot— actually do - How to find opportunities to apply AI to solve problems in your business organizations - What it's like to build machine learning and data science projects - How to work with an AI team and build an AI strategy in your company - How to navigate the ethical and public debate around AI Although this course is primarily non-technical, engineers can also take this course to learn about the business aspects of AI.

Explore the world of Artificial Intelligence (AI) with Microsoft's 12-week, 24-lesson curriculum! Dive into Symbolic AI, Neural Networks, Computer Vision, Natural Language Processing, and more. Hands-on lessons, quizzes, and labs enhance your learning. Perfect for beginners, this comprehensive guide, designed by experts, covers TensorFlow, PyTorch, and ethical AI principles. Start your AI journey today!" In this curriculum, you will learn: - Different approaches to Artificial Intelligence, including the "good old" symbolic approach with Knowledge Representation and reasoning (GOFAI). - Neural Networks and Deep Learning, which are at the core of modern AI. We will illustrate the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch. - Neural Architectures for working with images and text. We will cover recent models but may lack a little bit on the state-of-the-art. - Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems. What we will not cover in this curriculum: - Business cases of using AI in Business. Consider taking Introduction to AI for business users learning path on Microsoft Learn, or AI Business School, developed in cooperation with INSEAD. - Classic Machine Learning, which is well described in our Machine Learning for Beginners Curriculum. - Practical AI applications built using Cognitive Services. For this, we recommend that you start with modules Microsoft Learn for vision, natural language processing, Generative AI with Azure OpenAI Service and others. - Specific ML Cloud Frameworks, such as Azure Machine Learning, Microsoft Fabric, or Azure Databricks. Consider using Build and operate machine learning solutions with Azure Machine Learning and Build and Operate Machine Learning Solutions with Azure Databricks learning paths. - Conversational AI and Chat Bots. There is a separate Create conversational AI solutions learning path, and you can also refer to this blog post for more detail. - Deep Mathematics behind deep learning. For this, we would recommend Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, which is also available online at https://www.deeplearningbook.org/. For a gentle introduction to AI in the Cloud topics you may consider taking the Get started with artificial intelligence on Azure Learning Path. Go to github course page