MPP: Artificial Intelligence

Define the next generation of software

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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.

Summary

Who takes this course

Aspiring AI engineers

Difficulty

Advanced

Assessment

The courses have a final assessment with re-take restrictions. Successfully completing the final assessment will enable you to redeem your certificate of completion.

Certification

The Microsoft Professional Program is completed by completing the Capstone project. There isn’t an exam to complete this track.

Completion time

224-356 hours

Curriculum

Introduction to Artificial Intelligence (AI)

A high-level overview of AI to learn how Machine Learning provides the foundation for AI, and how you can leverage cognitive services in your apps.

Summary

Length
4 weeks (3 to 4 hours per week)
Level
Introductory
Language
English

About this course

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 companies 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, analyse, 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.

Prerequisites

  • High-school level math and statistics.
  • A basic knowledge of programming – Python would be an advantage, but is not essential.
  • A willingness to learn through exploration and perseverance.

Course Syllabus

  • Introduction
  • Machine Learning – The Foundation of AI
  • Text and Speech – Understanding Language
  • Computer Vision – Seeing the World Through AI
  • Bots – Conversation as a Platform
  • Next Steps
Introduction to Python for Data Science

The ability to analyse data with Python is critical in data science. Learn the basics, and move on to create stunning visualisations.

Summary

Length
6 weeks (2 to 4 hours per week)
Level
Introductory
Language
English

About this course

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, analysing 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 visualisations with Python and create your own stunning visualisations 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

Prerequisites

  • Some experience in working with data from Excel, databases, or text files.

Course Syllabus

Section 1: Python Basics

Take your first steps in the world of Python. Discover the different data types and create your first variable.

Section 2: Python Lists

Get the know the first way to store many different data points under a single name. Create, subset and manipulate Lists in all sorts of ways.

Section 3: Functions and Packages

Learn how to get the most out of other people’s efforts by importing Python packages and calling functions.

Section 4: Numpy

Write superfast code with Numerical Python, a package to efficiently store and do calculations with huge amounts of data.

Section 5: Matplotlib

Create different types of visualisations depending on the message you want to convey. Learn how to build complex and customised plots based on real data.

Section 6: Control flow and Pandas

Write conditional constructs to tweak the execution of your scripts and get to know the Pandas DataFrame: the key data structure for Data Science in Python.

Essential Math for Machine Learning: Python Edition

Learn the essential mathematical foundations for machine learning and artificial intelligence.

Summary

Length
6 weeks (6 to 8 hours per week)
Level
Intermediate
Language
English

About this course

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 Optimisation; 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 Optimisation
  • Vectors and Matrices
  • Statistics and Probability

Prerequisites

  • A basic knowledge of math
  • Some programming experience – Python is preferred.
  • A willingness to learn through self-paced study.

Course Syllabus

  • Introduction
  • Equations, Functions, and Graphs
  • Differentiation and Optimisation
  • Vectors and Matrices
  • Statistics and Probability
Ethics and Law in Data and Analytics

Analytics and AI are powerful tools that have real-word outcomes. Learn how to apply practical, ethical, and legal constructs and scenarios so that you can be an effective analytics professional.

Summary

Length
6 weeks (2 to 3 hours per week)
Level
Intermediate
Language
English

About this course

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
Data Science Research Methods: Python Edition

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

Summary

Length
6 weeks (2 to 3 hours per week)
Level
Intermediate
Language
English

About this course

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.

What you’ll learn

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

  • Data analysis and inference
  • Data science research design
  • Experimental data analysis and modeling

Prerequisites

  • A basic knowledge of math
  • Some programming experience – Python is preferred.
  • A willingness to learn through self-paced study.
Principles of Machine Learning: Python Edition

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

Summary

Length
6 weeks (6 to 8 hours per week)
Level
Intermediate
Language
English

About this course

Machine learning uses computers to run predictive models that learn from existing data in order to forecast future behaviours, 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.

What you’ll learn

  • Data exploration, preparation and cleaning
  • Supervised machine learning techniques
  • Unsupervised machine learning techniques
  • Model performance improvement

Prerequisites

  • A basic knowledge of math
  • Some programming experience – Python is preferred.
  • A willingness to learn through self-paced study.
Deep Learning Explained

Learn an intuitive approach to building the complex models that help machines solve real-world problems with human-like intelligence.

Summary

Length
6 weeks(4 to 8 hours per week)
Level
Intermediate
Language
English

About this course

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 Jupiter 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

Prerequisites

  • Basic programming skills
  • Working knowledge of data science
  • Skills equivalent to the following courses:

Course Syllabus

  • Week 1:

    Introduction to deep learning and a quick recap of machine learning concepts.

  • Week 2:

    Building a simple multi-class classification model using logistic regression

  • Week 3:

    Detecting digits in hand-written digit image, starting by a simple end-to-end model, to a deep neural network

  • Week 4:

    Improving the hand-written digit recognition with convolutional network

  • Week 5:

    Building a model to forecast time data using a recurrent network

  • Week 6:

    Building text data application using recurrent LSTM (long short term memory) units

Reinforcement Learning Explained

Learn how to frame reinforcement learning problems, tackle classic examples, explore basic algorithms from dynamic programming, temporal difference learning, and progress towards larger state space using function approximation and DQN (Deep Q Network).

Summary

Length
6 weeks (4 to 8 hours per week)
Level
Advanced
Language
English

About this course

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
Develop Applied AI Solutions (3 training options available)

Computer Vision and Image Analysis (Option 1)

A deep dive into Computer Vision, Image Analysis and Semantic Segmentation using the Microsoft Cognitive Toolkit.

Summary

Length
4 weeks(3 to 4 hours per week)
Level
Intermediate
Language
English

About this course

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.

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.

Prerequisites

  • Working knowledge of Python
  • Skills equivalent to the following courses:
    • DAT263x: Introduction to AI
    • DAT236x: Deep Learning Explained
Speech Recognition Systems (Option 2)

Learn about the pieces of a modern automatic speech recognition (ASR) system as we cover fundamental acoustic and linguistic theory, data preparation, language modeling, acoustic modeling, and decoding.

Summary

Length
4 weeks(5 to 6 hours per week)
Level
Advanced
Language
English

About this course

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

Prerequisites

  • Some python experience
  • Basic Machine Learning principles
  • Knowledge of probability and statistics
Natural Language Processing - NLP (Option 3)

A thorough introduction to cutting-edge technologies applied to Natural Language Processing.

Summary

Length
6 weeks(4 to 8 hours per week)
Level
Advanced
Language
English

About this course

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.

Prerequisites

  • Students need to have math and computer programming skills and fundamental knowledge on machine learning and deep learning before taking this course.
Microsoft Professional Capstone: Artificial Intelligence

The capstone project is offered directly by Microsoft and can only be done once per quarter: in January, April, July and October.

Enroll for the full MPP track here in the month prior to the one the capstone starts in, using your personal Microsoft account so that your progress is synced.

To have your progress synced with Azure Academy and to be eligible for the capstone project you have to have a Certificate of Completion for each one of 9 required courses from Azure Academy.

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