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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

Built with the focus of teaching students how to build deep learning predictive models for AI, the Microsoft Professional Program Certificate in Artificial Intelligence will help you learn the skills you need to build the intelligent future.

You'll Learn To

• 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

To be eligible to earn a certificate for completing the Microsoft Professional Program in Artificial Intelligence, please go to https://academy.microsoft.com/en-us/register/ to create a Microsoft Academy account. After signing up, you’ll be able to track your progress on a personalized dashboard that updates every time you earn a Verified Certificate in a course from Artificial Intelligence track.

How It Works

This comprehensive curriculum features courses that are presented in a suggested order that builds your skills as you advance through the courses.  While the order is a suggestion you are free to take the courses in any order that you wish. Upon completing the coursework, you will be able to demonstrate what you have learned through completion of the Capstone project. The first eight courses are required, you may then choose from four options for the ninth course prior to completing the Professional Capstone. 

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Artificial Intelligence Professional Program - Curriculum Overview

Course 1 - Introduction to Artificial Intelligence (AI)

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.

Note: The practical elements of this course are based on Microsoft Azure, and require an Azure subscription. Instructions for signing up for a free trial subscription are provided with the course materials, or you can use an existing Azure subscription if you have one.

What you'll learn

In this course, you will learn how to:

• Build simple machine learning models with Azure Machine Learning;
• Use Python and Microsoft cognitive services to work with text, speech, images, and video;
• Use the Microsoft Bot Framework to implement conversational bots.

Course 2 - Introduction to Python for Data Science

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.

What you'll learn

• Explore Python language fundamentals, including basic syntax, variables, and types
• Create and manipulate regular Python lists
• Use functions and import packages
• Build Numpy arrays, and perform interesting calculations
• Create and customize plots on real data
• Supercharge your scripts with control flow, and get to know the Pandas DataFrame

Course 3 - Essential Mathematics for 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.

What you'll learn

After completing this course, you will be familiar with the following mathematical concepts and techniques:

• Equations, Functions, and Graphs
• Differentiation and Optimization
• Vectors and Matrices
• Statistics and Probability

Course 4 - Ethics and Law in Data and Analytics

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.

What you'll learn

• Foundational abilities in applying ethical and legal frameworks for the data profession
• Practical approaches to data and analytics problems, including Big Data and Data Science and AI
• Applied data methods for ethical and legal work in Analytics and AI

Course 5 - Data Science Essentials

Demand for data science talent is exploding. Develop your career as a data scientist, as you explore essential skills and principles with experts from Duke University and Microsoft.

In this data science course, you will learn key concepts in data acquisition, preparation, exploration, and visualization taught alongside practical application oriented examples such as how to build a cloud data science solution using Microsoft Azure Machine Learning platform, or with R, and Python on Azure stack.

What you'll learn

• Explore the data science process
• Probability and statistics in data science
• Data exploration and visualization
• Data ingestion, cleansing, and transformation
• Introduction to machine learning
• The hands-on elements of this course leverage a combination of R, Python, and Microsoft Azure Machine Learning

Course 6 - Principles of Machine Learning

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 R, Python, and Azure Machine Learning.

What you'll learn

• Explore classification
• Regression in machine learning
• How to improve supervised models
• Details on non-linear modeling
• Clustering
• Recommender systems
• The hands-on elements of this course leverage a combination of R, Python, and Microsoft Azure Machine Learning

Course 7 - Deep Learning Explained

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.

What you'll learn

• The components of a deep neural network and how they work together
• The basic types of deep neural networks (MLP, CNN, RNN, LSTM) and the type of data each is designed for
• A working knowledge of vocabulary, concepts, and algorithms used in deep learning
• How to build:
  • An end-to-end model for recognizing hand-written digit images, using a multi-class Logistic Regression and MLP (Multi-Layered Perceptron)
  • A CNN (Convolution Neural Network) model for improved digit recognition
  • An RNN (Recurrent Neural Network) model to forecast time-series data
  • An LSTM (Long Short Term Memory) model to process sequential text data

Course 8 - Reinforcement Learning Explained

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.

What you'll learn

• Reinforcement Learning Problem
• Markov Decision Process
• Bandits
• Dynamic Programming
• Temporal Difference Learning
• Approximate Solution Methods
• Policy Gradient and Actor Critic
• RL that Works

Course 9a - Knowledge Graphs - TBA 

Course 9b - Computer Vision and Image Analysis

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.

Release schedule:
This course is of rolling release model, there are 5 modules in this course, M00,M01 and M02 are released when the course is live, other modules will be released according to the following schedule:

• 4/17/2018: Module 3.
• 4/25/2018: Module 4, final exam

What you'll learn

• Apply classical Image Analysis techniques, such as  Edge Detection,  Watershed and Distance Transformation as well as K-means Clustering to segment a basic dataset.
• Implement classical Image Analysis algorithms using the OpenCV library.
• Compare classical and Deep-Learning object classification techniques.
• Apply Microsoft ResNet, a deep Convolutional Neural Network (CNN) to object classification using the Microsoft Cognitive Toolkit.
• Apply Transfer Learning to augment ResNet18 for a Fully Convolutional Network (FCN) for Semantic Segmentation.

Course 9c - Speech Recognition Systems .

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.

What you'll learn

• Fundamentals of Speech Recognition
• Basic Signal Processing for Speech Recogntion
• Acoustic Modeling and Labeling
• Common Algorithms for Language Modeling
• Decoding Acoustic Features into Speech

Course 9d - 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.

What you'll learn

• Apply deep learning models to solve machine translation and conversation problems.
• Apply deep structured semantic models on information retrieval and natural language applications.
• Apply deep reinforcement learning models on natural language applications.
• Apply deep learning models on image captioning and visual question answering.

Course 10 - Microsoft Professional Capstone: Artificial Intelligence

Showcase the knowledge and skills 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.

What you'll learn

This course is unusual in that it is a test of the knowledge and skills you have developed by taking other courses. The point of this capstone project is to enable to to gain experience of applying these skills to solve a real problem.

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Courses

 9 courses + Final Project

Effort

 8 - 16 hours per course

Price

 $99 per course / $990 for the entire program