Learn the basics of Python programming. Starting from the very beginning, with basic arithmetic and variables, and learn how to handle data structures, such as Python lists, Numpy arrays, and Pandas DataFrames. Along the way, you’ll learn about Python functions and control flow. Plus, you’ll look at the world of data visualizations with Python and create your own stunning visualizations based on real data.

Learn the essential mathematical foundations for machine learning and artificial intelligence using Python. The couse focuses on mathematical concepts that you’ll encounter in studies of machine learning. It is designed to fill the gaps for students who missed these key concepts as part of their formal education, or who need to refresh their memories after a long break from studying math.

Learn to apply ethical and legal frameworks to initiatives in the data profession. You will explore practical approaches to data and analytics problems posed by work in Big Data, Data Science, and AI. You will also investigate applied data methods for ethical and legal work in Analytics and AI.

Learn the essential skills and hands-on experience with the science and research aspects of data science work using Python, from setting up a proper data study to making valid claims and inferences from data experiments.

Get hands-on experience building and deriving insights from machine learning models using Python and Azure Notebooks.

Deep learning is a key enabler of AI powered technologies being developed across the globe. In this deep learning course, you will learn an intuitive approach to building complex models that help machines solve real-world problems with human-like intelligence. The intuitive approaches will be translated into working code with practical problems and hands-on experience. You will learn how to build and derive insights from these models using Python Jupyter notebooks running on your local Windows or Linux machine, or on a virtual machine running on Azure. You will learn how to use the Microsoft Cognitive Toolkit to harness the intelligence within massive datasets through deep learning with uncompromised scaling, speed, and accuracy.

Reinforcement Learning (RL) is an area of machine learning, where an agent learns by interacting with its environment to achieve a goal.In this course, you will be introduced to the world of reinforcement learning. You will learn how to frame reinforcement learning problems and start tackling classic examples like news recommendation, learning to navigate in a grid-world, and balancing a cart-pole. You will explore the basic algorithms from multi-armed bandits, dynamic programming, TD (temporal difference) learning, and progress towards larger state space using function approximation, in particular using deep learning. You will also learn about algorithms that focus on searching the best policy with policy gradient and actor critic methods. Along the way, you will get introduced to Project Malmo, a platform for Artificial Intelligence experimentation and research built on top of the Minecraft game.

Computer Vision and Image Analysis

Learn about Image Analysis techniques using OpenCV and the Microsoft Cognitive Toolkit to segment images into meaningful parts. You will explore the evolution of Computer Vision, from classical to Deep-Learning techniques using Transfer Learning and Microsoft ResNet to train a model to perform Semantic Segmentation.

Speech Recognition Systems

This course is designed to give an overview of the components of a modern Automatic Speech Recognition (ASR) system. In each lecture, we describe a component's purpose and general structure. In each lab, the student creates a functioning block of the system. At the end of the course, we will have built a speech recognition system almost entirely out of Python code.

Natural Language Processing (NLP)

Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. In this course, you will be given a thorough overview of Natural Language Processing and how to use classic machine learning methods. You will learn about Statistical Machine Translation as well as Deep Semantic Similarity Models (DSSM) and their applications. We will also discuss deep reinforcement learning techniques applied in NLP and Vision-Language Multimodal Intelligence.

Validate the skills and knowledge you’ve acquired during the Microsoft Professional Program for Artificial Intelligence, and solve a real-world AI problem in this program capstone project. The project takes the form of a challenge in which you will develop a deep learning solution that is tested and scored to determine your grade.Note: This course assumes you have completed the previous courses in the Microsoft Professional Program for Artificial Intelligence.