This is a self-paced lab that takes place in the Google Cloud console. In this lab you will build several Data Pipelines that will ingest data from a publicly available dataset into BigQuery.

Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. In this course, you will learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions. This is the third course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Each course teaches you the concepts and skills that are measured by the exam. This Specialization is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. It teaches data scientists how to create end-to-end solutions in Microsoft Azure. Students will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions, and implement responsible machine learning. They will also learn to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.

Hello everyone and welcome to this hands-on guided project on Artificial intelligence (AI)-powered chest disease detection and classification. AI has been revolutionizing healthcare and medicine in many areas such as: (1) Medical imagery, (2) Drug research, and (3) Genome development. Deep learning has been proven to be superior in detecting and classifying disease using imagery data. In this case study, we will automate the process of detecting and classifying chest disease from X-Ray images to reduce the cost and time of detection. This guided project is practical and directly applicable to the healthcare industry. You can add this project to your portfolio of projects which is essential for your next job interview.

In this guided project, we will explore plotly express to visualize statistical plots such as box plots, histograms, heatmaps, density maps, contour plots, and violin plots. Plotly express is a super powerful Python package that empowers anyone to create, manipulate and render graphical figures. This crash course is super practical and directly applicable to many industries such as banking, finance and tech industries. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this 1-hour long project-based course, you will learn how to create a simple linear regression algorithm and use it to solve a basic regression problem. By the end of this project, you will have built, trained, tested, and visualized a Regression model that will be able to accurately predict the salary of a data scientist if provided with some information about years of experience. In order to be successful in this project, you should just know the basics of R and linear regression.

Practice makes perfect. It’s true for people learning to master a new skill, and it’s also true for your AI brain. Just as you need the right environment to practice, get feedback and try again, so does your AI brain. In this course, you’ll solve industrial engineering problems inspired by real problems your instructors have worked on in industry. You’ll learn how to build, test and deploy an AI brain using Microsoft Bonsai, a cloud-based, low-code platform. We’ll walk through the entire Bonsai platform from setup to deployment. Along the way, you’ll use Bonsai to conduct machine teaching experimentation to train a brain and assess its progress. Because you’ll be teaching the brain a relatively complex task, you’ll run multiple simulations until you’re satisfied with the results. You’ll then prep the brain for graduation into the real world — deploying it into a machinery control system or other live environment. At the end of this course, you’ll be able to: • Build an autonomous AI that combines reinforcement learning with machine learning, expert rules and other methods that you’ve used in the first two courses of the specialization • Establish requirements for a simulated environment for your brain to practice a task • Validate and assess your brain’s performance of a task and make improvements to your brain design • Evaluate whether a simulator is a good practice environment • Deploy a brain on a real piece of hardware This course requires an Azure subscription. This course is part of a specialization called Autonomous AI for Industry, which will launch in early 2023.

This course will introduce MIMIC-III, which is the largest publicly Electronic Health Record (EHR) database available to benchmark machine learning algorithms. In particular, you will learn about the design of this relational database, what tools are available to query, extract and visualise descriptive analytics. The schema and International Classification of Diseases coding is important to understand how to map research questions to data and how to extract key clinical outcomes in order to develop clinically useful machine learning algorithms.

Welcome to Introduction to Data, Signal, and Image Analysis with MATLAB! MATLAB is an extremely versatile programming language for data, signal, and image analysis tasks. This course provides an introduction on how to use MATLAB for data, signal, and image analysis. After completing the course, learners will understand how machine learning methods can be used in MATLAB for data classification and prediction; how to perform data visualization, including data visualization for high dimensional datasets; how to perform image processing and analysis methods, including image filtering and image segmentation; and how to perform common signal analysis tasks, including filter design and frequency analysis.

Welcome to Fundamentals of Data Warehousing, the third course of the Key Technologies of Data Analytics specialization. By enrolling in this course, you are taking the next step in your career in data analytics. This course is the third of a series that aims to prepare you for a role working in data analytics. In this course, you will be introduced to many of the core concepts of data warehousing. You will learn about the primary components of data warehousing. We’ll go through the common data warehousing architectures. The hands-on material offers to add storage to your cloud environment and configure a database. This course covers a wide variety of topics that are critical for understanding data warehousing and are designed to give you an introduction and overview as you begin to build relevant knowledge and skills.

In this hands-on project, we will understand the fundamentals of data visualization with Python and leverage the power of two important python libraries known as Matplotlib and seaborn. We will learn how to generate line plots, scatterplots, histograms, distribution plot, 3D plots, pie charts, pair plots, countplots and many more! Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.