Artificial Intelligence Learning Track

Artificial Intelligence Learning Track

Microsoft LT-AI

Free of Charge

Build the Intelligent Future.

Artificial Intelligence (AI) will define the next generation of software solutions. Human-like capabilities such as understanding natural language, speech, vision, and making inferences from knowledge will extend software beyond the app. The AI track takes aspiring AI engineers from a basic introduction of AI to mastery of the skills needed to build deep learning models for AI solutions that exhibit human-like behavior and intelligence

What you will learn

  • Use Python to work with Data
  • Consider Ethics for AI
  • Build Machine Learning Models
  • Build Reinforcement Learning Models
  • Develop Applied AI Solutions
  • Operationalize AI Solutions

What’s included

Artificial Intelligence Learning Track - Curriculum Overview

  • Course 1 - Introduction to Artificial Intelligence (AI)
  • Course 2 - Introduction to Python for Data Science
  • Course 3 - Essential Mathematics for Artificial Intelligence: Python Edition
  • Course 4 - Ethics and Law in Data and Analytics
  • Course 5 - Data Science Research Methods: Python Edition
  • Course 6 - Principles of Machine Learning: Python Edition
  • Course 7 - Deep Learning Explained
  • Course 8 - Reinforcement Learning Explained
  • Course 9a - Computer Vision and Image Analysis   |   Course 9b - Speech Recognition Systems   |   Course 9c - Natural Language Processing (NLP)
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Free of Charge

Introduction to Artificial Intelligence

Course 1

Introduction to Artificial Intelligence (AI)

Introduction to Artificial Intelligence

Microsoft DAT263x
Free of Charge
Microsoft
Open edX
90 days

Artificial Intelligence will define the next generation of software solutions. This computer science course provides an overview of AI, and explains how it can be used to build smart apps that help organizations be more efficient and enrich people’s lives. It uses a mix of engaging lectures and hands-on activities to help you take your first steps in the exciting field of AI.

Discover how machine learning can be used to build predictive models for AI. Learn how software can be used to process, analyze, and extract meaning from natural language; and to process images and video to understand the world the way we do. Find out how to build intelligent bots that enable conversational communication between humans and AI systems.

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Introduction to Python for Data Science

Course 2

Introduction to Python for Data Science

Introduction to Python for Data Science

Microsoft DAT208x
Free of Charge
Microsoft
Open edX
90 days

Python is a very powerful programming language used for many different applications. Over time, the huge community around this open source language has created quite a few tools to efficiently work with Python. In recent years, a number of tools have been built specifically for data science. As a result, analyzing data with Python has never been easier.


In this practical course, you will start 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.

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Essential Math for Machine Learning: Python Edition

Course 3

Essential Mathematics for Artificial Intelligence

Essential Math for Machine Learning: Python Edition

Microsoft DAT256x
Free of Charge
Microsoft
Open edX
90 days

This course is part of the Microsoft Professional Program in Artificial Intelligence.

Want to study machine learning or artificial intelligence, but worried that your math skills may not be up to it? Do words like “algebra’ and “calculus” fill you with dread? Has it been so long since you studied math at school that you’ve forgotten much of what you learned in the first place?

You’re not alone. machine learning and AI are built on mathematical principles like Calculus, Linear Algebra, Probability, Statistics, and Optimization; and many would-be AI practitioners find this daunting. This course is not designed to make you a mathematician. Rather, it aims to help you learn some essential foundational concepts and the notation used to express them. The course provides a hands-on approach to working with data and applying the techniques you’ve learned.

This course is not a full math curriculum; it’s not designed to replace school or college math education. Instead, it focuses on the key 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.

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Ethics and Law in Data and Analytics

Course 4

Ethics and Law in Data and Analytics

Ethics and Law in Data and Analytics

Microsoft DAT249x
Free of Charge
Microsoft
Open edX
90 days

Corporations, governments, and individuals have powerful tools in Analytics and AI to create real-world outcomes, for good or for ill.

Data professionals today need both the frameworks and the methods in their job to achieve optimal results while being good stewards of their critical role in society today.

In this course, you'll learn to apply ethical and legal frameworks to initiatives in the data profession. You'll explore practical approaches to data and analytics problems posed by work in Big Data, Data Science, and AI. You'll also investigate applied data methods for ethical and legal work in Analytics and AI.

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Data Science Research Methods: Python Edition

Course 5

Data Science Essentials

Data Science Research Methods: Python Edition

Microsoft DAT273x
Free of Charge
Microsoft
Open edX
90 days

Data scientists are often trained in the analysis of data. However, the goal of data science is to produce a good understanding of some problem or idea and build useful models on this understanding. Because of the principle of "garbage in, garbage out," it is vital that a data scientist know how to evaluate the quality of information that comes into a data analysis. This is especially the case when data are collected specifically for some analysis (e.g., a survey).

In this course, you will learn the fundamentals of the research process-from developing a good question to designing good data collection strategies to putting results in context. Although a data scientist may often play a key part in data analysis, the entire research process must work cohesively for valid insights to be gleaned.

Developed as a powerful and flexible language used in everything from Data Science to cutting-edge and scalable Artificial Intelligence solutions, Python has become an essential tool for doing Data Science and Machine Learning. With this edition of Data Science Research Methods, all of the labs are done with Python, while the videos are language-agnostic. If you prefer your Data Science to be done with R, please see Data Science Research Methods: R Edition.

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Principles of Machine Learning: Python Edition

Course 6

Build Machine Learning Models

Principles of Machine Learning: Python Edition

Microsoft DAT275x
Free of Charge
Microsoft
Open edX
90 days

Machine learning uses computers to run predictive models that learn from existing data in order to forecast future behaviors, outcomes, and trends.

In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using Python, and Azure Notebooks.

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Deep Learning Explained

Course 7

Build Deep Learning Models

Deep Learning Explained

Microsoft DAT236x
Free of Charge
Microsoft
Open edX
90 days

Machine learning uses computers to run predictive models that learn from existing data to forecast future behaviors, outcomes, and trends. Deep learning is a sub-field of machine learning, where models inspired by how our brain works are expressed mathematically, and the parameters defining the mathematical models, which can be in the order of few thousands to 100+ million, are learned automatically from the data.

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. Alternatively, you can leverage the Microsoft Azure Notebooks platform for free.

This course provides the level of detail needed to enable engineers / data scientists / technology managers to develop an intuitive understanding of the key concepts behind this game changing technology. At the same time, you will learn simple yet powerful “motifs” that can be used with lego-like flexibility to build an end-to-end deep learning model. You will learn how to use the Microsoft Cognitive Toolkit — previously known as CNTK — to harness the intelligence within massive datasets through deep learning with uncompromised scaling, speed, and accuracy.

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Reinforcement Learning Explained

Course 8

Build Reinforcement Learning Models

Reinforcement Learning Explained

Microsoft DAT257x
Free of Charge
Microsoft
Open edX
90 days

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.

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Computer Vision and Image Analysis

Course 9

Develop Applied AI Solutions (choose one)

Computer Vision and Image Analysis

Microsoft DEV290x
Free of Charge
Microsoft
Open edX
90 days

Computer Vision is the art of distilling actionable information from images.

In this hands-on course, we’ll learn about Image Analysis techniques using OpenCV and the Microsoft Cognitive Toolkit to segment images into meaningful parts. We’ll explore the evolution of Image Analysis, from classical to Deep-Learning techniques.

We’ll use Transfer Learning and Microsoft ResNet to train a model to perform Semantic Segmentation.

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Speech Recognition Systems

Microsoft DEV287x
Free of Charge
Microsoft
Open edX
90 days

Developing and understanding Automatic Speech Recognition (ASR) systems is an inter-disciplinary activity, taking expertise in linguistics, computer science, mathematics, and electrical engineering.

When a human speaks a word, they cause their voice to make a time-varying pattern of sounds. These sounds are waves of pressure that propagate through the air. The sounds are captured by a sensor, such as a microphone or microphone array, and turned into a sequence of numbers representing the pressure change over time. The automatic speech recognition system converts this time-pressure signal into a time-frequency-energy signal. It has been trained on a curated set of labeled speech sounds, and labels the sounds it is presented with. These acoustic labels are combined with a model of word pronunciation and a model of word sequences, to create a textual representation of what was said.

Instead of exploring one part of this process deeply, this course is designed to give an overview of the components of a modern 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.

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Natural Language Processing (NLP)

Microsoft DEV288x
Free of Charge
Microsoft
Open edX
90 days

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.

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