Learn how to discover hidden patterns from the raw data, about machines that can perform task that usually only people can and more. This course will introduce you to key Artificial Intelligence and Data science concepts and you will learn fundamental terminology of basic artificial intelligence and Data science such as Machine Learning, Big Data, Supervised Learning, Unsupervised Learning, Data Science, Artificial Intelligence, Artificial Neural Networks and Deep Learning.

In this learning path, you will learn about cloud concepts, understand the benefits of cloud computing in Azure and how it can save you time and money.

In this learning path you will write your first lines of Python code, store and manipulate data to modify its type and appearance, execute built-in functionality available from libraries of code and add logic to your code to enable complex business functionality.

A high-level overview of artificial intelligence (AI) and machine learning aimed at people with little or no knowledge of computer science and statistics.

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

Reinforcement Learning (RL) is an area of machine learning, where an agent learns by interacting with its environment to achieve a goal. Learn how to frame reinforcement learning problems, tackle classic examples and explore basic algorithms from dynamic programming.

Natural language processing supports applications that can see, hear, speak with, and understand users. Using text analytics, translation, and language understanding services, Microsoft Azure makes it easy to build applications that support natural language.

Interested in learning a programming language but aren’t sure where to start? Start here! Learn the basic syntax and thought processes required to build simple applications using C#.

The student will learn about the available Cognitive Services on Microsoft Azure and their role in architecting AI solutions.

The student will learn about the Microsoft Bot Framework and Bot Services.

The student will learn about the QnA Maker and how to integrate Bots and QnA Maker to build up a useful knowledge base for user interactions.

The student will learn about integrating LUIS with a Bot to better understand the users’ intentions when interacting with the Bot.

The student will learn about integrating Bots and Agents with Azure Cognitive Services for advanced features such as sentiment analysis, image and text analysis, and OCR and object detection.

In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.

In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.

Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.

One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you’ll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.

Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it’s time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you’ll explore how to define and run them in this module.

Models are designed to help decision making through predictions, so they’re only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.

By this stage of the course, you’ve learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you’ll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.

Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It’s increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model’s behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.

After a model has been deployed, it’s important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.