Robotics & Autonomous Systems

A free, self-paced learning path designed to help educators explore the potential of artificial intelligence in education. It covers essential AI concepts, techniques, tools, and practical applications to enhance teaching and learning experiences.

This course explores the application of Artificial Intelligence in the context of autonomous vehicles and robotics, covering key concepts and techniques in this rapidly growing field.

This course provides an introduction to the engineering of autonomous aerospace systems, with a focus on drones. It covers topics such as modeling, simulation, and control of autonomous vehicles.

Learn the fundamentals of robotics and how to leverage AWS services for developing and deploying robotics applications.

This professional certificate program equips you with the skills to launch software products using Microsoft tools like Copilot and Power BI. It covers B2B market research, cloud strategy, product launch, and stakeholder management, with a specific course on 'Product Strategy and Roadmapping'.

A comprehensive professional certificate that covers the entire UX design process, with a specific focus on accessibility and inclusive design. It also teaches how to leverage AI to enhance UX design efficiency and creativity.

This Specialization provides a rigorous treatment of robotics, covering mechanics, planning, and control. It is intended for students with a desire to learn the foundational principles of modern robotics.

Autonomous Vehicles and Self-Driving Cars

A 12-week, 24-lesson curriculum all about Artificial Intelligence for beginners from Microsoft.

Lecture 1: Introduction Here you will find a short introduction to the course. We outline the objectives, structure, and practical outcomes. This sets the stage for a hands-on experience in machine learning with EEG signals.Lecture 2: Connect to Google Colab This chapter provides a step-by-step guide on how to connect to and work in Google Colab. You’ll learn how to set up your environment, install required libraries, and ensure you are ready to run the code examples provided throughout the course.Lecture 3: Hardware for Brain-Computer Interface This chapter covers the essential hardware used in EEG-based brain-computer interfaces. Lecture 4: Data Evaluation We dive into evaluating the quality of your EEG data. This chapter explores techniques to inspect, clean, and annotate EEG recordings, ensuring that your data is reliable before moving forward with analysis or machine learning tasks.Lecture 5: Prepare the Dataset Learn how to transform raw EEG signals into structured datasets suitable for machine learning. This chapter includes labeling, segmenting, and feature extraction techniques—critical steps for successful model training and testing.Lecture 6: Introduction to DL In this chapter, we introduce the fundamentals of deep learning and explain why Keras is a suitable library for working with EEG data. You’ll gain a basic understanding of deep learning concepts, how they apply to EEG signal processing, and where to find more information about Keras and its capabilities. This sets the foundation for implementing neural networks in upcoming lectures.Lecture 7. Convolutional Neural Networks (CNNs) for EEG This chapter introduces convolutional neural networks (CNNs) and their application to EEG signal processing. You’ll learn the theory behind CNNs, how they are used for automatic feature extraction, and how to i

Welcome to this course on Data Science and Machine Learning with Microsoft Azure. You would learn various lessons for Data Visualization, Data Cleaning and Data Analysis using Microsoft Power BI. It is a powerful Business Intelligence software that can be used for various domains ranging from creating Analytics dashboard and Business Intelligence reports to fetching information from wide range of data sources. You could also perform various types of data cleaning operations using Power Query. Moreover, if you want to create some advanced types of Analytics charts you can write a few lines of code in python using frameworks such as Matplotlib and Seaborn. And if you want to modify the dataset either by creating derived values based on certain mathematical formula or specified conditions you could perform various Data Modelling operations as well by using creating Calculated fields and by using Power Query editor. In this course you would learn various such concepts with completely practical examples on Power BI Desktop, that can be applied in the similar way on azure cloud.After you have learned various lessons on Power BI, you would be learning Azure Machine learning in the later sections of this course. Here you would learn to analyze an image using Computer Vision. And you would also learn to perform language detection, sentiment analysis, key phrase extraction and entity recognition using Azure Text Analytics. Here in this course you will learn following lessons on Data Science using Microsoft Power BI-Creating Visualization charts such as Bar chart Pie chart Donut or Ring chart Treemap chart Interactive charts and Drill down Table and Matrix Date and other Slicers Creating a calculated field Gauge chart Map chart and modes Scatterplot and Animation Playback Basics of Power Query Row deletion and