Advanced C++ is the third course in the Microsoft Learning C++ series. Students will learn about a vide variety of high-level C++ software development techniques.It is highly recommended that students complete both the “Introduction to C++” and “Intermediate C++” courses before attempting this one. However, with a solid understanding of the prerequisites, it is not absolutely necessary to take these courses. Prerequisites include an understanding of pointers, memory allocation, file processing, and general OOP concepts.This course will cover the following concepts: Exceptions, C++ templates, Iterators and advanced Class mechanics, and design patterns
ABOUT THIS COURSEWhen you create real-world applications, the ability to store information in your program code is critically important. In this course, you will learn how programming languages make use of various data structures to hold this information. For example, storing a list of values for countries. You ill learn how C# provides a plethora of data structures from simple arrays to more complex structures that permit the use of “typing”. Generics is a concept that C# uses to allow the representation of objects in your data structures to apply “typing”, making it easier to work with specific data types.Even though most programming languages today implement their own versions of sorting and searching algorithms, these examples still provide you with a solid foundation for understanding the logic behind these algorithms and can shed light on how to implement your own algorithms in later programs. Not to mention the fact that they are also some of the most common algorithms used in interviews for programming jobs.This course will provide you with a solid foundation in the use of data structures and algorithms using the C# language.PREREQUISITESIntroduction to C#Object-Oriented Programming in C#WHAT YOU'LL LEARNC# simple and complex data structuresHow to implement the various data structures in C#How to implement sort and search algorithms in C#
This course is part of the Microsoft Professional Program in Artificial Intelligence.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.
This course is part of the Microsoft Professional Program Certificate in Data Science and Microsoft Professional Program in Artificial Intelligence.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.
This course is part of the Microsoft Professional Program in Artificial Intelligence.achine 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.
This course is part of the Microsoft Professional Program Certificate in Data Science and Microsoft Professional Program in Artificial Intelligence.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.
This course is the second in a three-part series designed to teach students some of the most important C++ concepts. This part focuses on how C++ interacts with memory, featuring concepts like pointers/memory addresses, heap memory management, and writing/reading files.Knowing C++, you can create applications that will run on a wide variety of hardware platforms such as personal computers running Windows, Linux, UNIX, and Mac OS X, as well as small form factor hardware such as IoT devices like the Raspberry PI and Arduino –based boards.
This course is part of the Microsoft Professional Program in Artificial Intelligence.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.
This course provides an introduction to Microsoft’s ASPNET Web API framework for building RESTful HTTP services. It begins by explaining the benefits of a RESTful HTTP service. Then, through a combination of demonstrations and labs, the course proceeds to teach the student how to implement a RESTful HTTP service. By the end of the course, the student will have hands on experience with building and consuming a web service that can read and write complex data types over HTTP.
ABOUT THIS COURSEThere are many programming languages in use today. Choosing which language to program in can be based on many factors such as learning curve, job specific requirements, platform specifics, or a plethora of other criteria. In this course, you will be introduced to the C# language and the world of .NET programming.The C# programming language was created from the ground up to be an object-oriented programming language that offers ease of use, familiarity to C/C++ and Java developers, along with enhanced memory and resource management. C# is prevalent on the Microsoft platform but is also being used to develop software that runs on Linux, Android, and iOS devices as well.Learning C# can position you for future programming opportunities, provide a solid foundation in object-oriented programming knowledge, and pave the way for learning other programming languages. This course aims to teach you about the core aspects of the C# language.This course is the first part of a three-part series designed to teach core C# language fundamentals. In the second course of the series, you will learn object-oriented programming concepts and the third course offers instruction on data structures and algorithms with C#. Once you complete the series, you will have a very good foundation of C# knowledge to expand on and learn more as you progress in your programming career or hobby.WHAT YOU'LL LEARNC# SyntaxC# Language FundamentalsIteration in C#Making decisions in C# code
C++ is a general purpose programming language that supports various computer programming models such as object-oriented programming and generic programming. It was created by Bjarne Stroustrup and, “Its main purpose was to make writing good programs easier and more pleasant for the individual programmer.”*By learning C++, you can create applications that will run on a wide variety of hardware platforms such as personal computers running Windows, Linux, UNIX, and Mac OS X, as well as small form factor hardware such as IoT devices like the Raspberry PI and Arduino–based boards.
In this Introduction to GitHub course, we will examine the differences between Git and GitHub. We will also learn the following:A workflow process called GitHub Flow as well as the steps within that workflowHow to resolve merge conflicts in GitHubHow to fork a repository to make it your ownHow GitHub includes some powerful management components allowing you to create project boards and milestones.
This course is an introduction to the fundamental concepts and the skills necessary to design, read, and write applications. Essential programming skills are taught with the emphasis on the creation of general applications utilizing major class libraries. Basic programming structures and program building blocks will be covered. Object-based programming techniques will be discussed. This course will help you prepare for Exam 98-388 Introduction to Programming Using Java.
This course is part of the Microsoft Professional Program Certificate in Data Science and Microsoft Professional Program in Artificial Intelligence.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.
This course is part of the Microsoft Professional Program in Artificial Intelligence.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.
ABOUT THIS COURSEMany mainstream programming languages in use today, support the concept of object-oriented programming. Modeling real-world objects in your code allows you to create more robust and effective applications.C# was designed from the ground up to be an object-oriented, type-safe programming language. In this course, you will build on the fundamentals that were covered in Introduction to C#. You will extend your knowledge by applying core OOP principles to the code and applications you will create in this course. You will build a knowledge of encapsulation, inheritance and polymorphism. You will also learn memory management in the .NET framework.WHAT YOU'LL LEARNCore object-oriented programming conceptsHow to create and use classes and objects in a C# applicationApplying the three core OOP concepts using C#A grasp of memory and resource management in C# and the .NET FrameworkPREREQUISITESIntroduction to C#
This course is part of the Microsoft Professional Program Certificate in Data Science and Microsoft Professional Program in Artificial Intelligence.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.
This course is part of the Microsoft Professional Program in Artificial Intelligence.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.
This course is part of the Microsoft Professional Program in Artificial Intelligence.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.
In this lab, you will develop a HTML5 web application and observe how it is rendered inside a desktop browser.
In this lab, you will take the role of a GitHub administrator for new GitHub repositories. You will learn how to create and manage repositories as well as how to perform all the functions of GitHub Flow using the GitHub.com website in additional to the command line Git tools.
In this lab, you will use GitHub and the Microsoft Azure cloud platform together to build an application lifecycle management (ALM) environment. You will use GitHub to fork a repository and manage a project with issues and tasks. You will use Microsoft Azure as the deployment target for your forked application and will set up continuous delivery so that your resolution of the issues in the GitHub, along with the associated source code commits, will trigger automatic deployment to Azure for immediate verification of the fixes.
In this lab, you will use Visual Studio to learn the fundamentals of building out web applications using DotNet Core. You will learn about using the Razor language to create views, and create controllers behind the scenes. This lab will also cover fundamentals such as configuring routing, using data models, and basic data access using Entity Framework (EF).
In this lab, you will use the various features of Bootstrap to build a simple website. You will add Bootstrap components (such as modals, tooltips and more), define a layout, and implement different Bootstrap extensions.
In this lab, you will use a virtual machine that has Visual Studio 2017 pre-installed and configured to learn the fundamentals of programming with C#.You will learn about the following topics through a series of hands-on exercises: Data Types, Operators, Expressions, Loops, Conditional Logic, Casting, Arrays, Namespaces and setting breakpoints using the Visual Studio Debugger.
In this lab, you will use Visual Studio to create several programs that explore capabilities of the .NET Framework. You will learn the basics of using file I/O, execute multiple tasks asynchronously, encrypt and decrypt data, as well as understanding the basics of using LINQ queries. The lab exercise will close out by understanding how to use the IDisposable interface to control the lifecycle of your objects.
Responsive Web Design is the ability or capacity of a web app to adapt to any viewport. Nowadays it's required for a website to be responsive, you as a developer have to expect for your webpage to be opened by users on any kind of device: phone, tablet or desktop. The RWD contains 3 main elements that you'll need to integrate in your website to make it responsive: CSS queries, responsive images and fluid grids. In this lab you'll find out the main parts of developing a responsive web app.