In this 2-hour long guided project, we will use a ResNet-18 model and train it on a COVID-19 Radiography dataset. This dataset has nearly 3000 Chest X-Ray scans which are categorized in three classes - Normal, Viral Pneumonia and COVID-19. Our objective in this project is to create an image classification model that can predict Chest X-Ray scans that belong to one of the three classes with a reasonably high accuracy. Please note that this dataset, and the model that we train in the project, can not be used to diagnose COVID-19 or Viral Pneumonia. We are only using this data for educational purpose. Before you attempt this project, you should be familiar with programming in Python. You should also have a theoretical understanding of Convolutional Neural Networks, and optimization techniques such as gradient descent. This is a hands on, practical project that focuses primarily on implementation, and not on the theory behind Convolutional Neural Networks. 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 project, we will analyze life expectancy data by performing data wrangling & exploratory data analysis (EDA). Pandas is a powerful open source data analysis tools in python. Exploratory Data Analysis (EDA) is a process of analyzing data to gain valuable insights such as statistical summary & visualizations.

In this 1-hour long project-based course, you will be able to: - Understand the theory and intuition behind Convolutional Neural Networks (CNNs). - Import Key libraries, dataset and visualize images. - Perform image normalization and convert from color-scaled to gray-scaled images. - Build a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend. - Compile and fit Deep Learning model to training data. - Assess the performance of trained CNN and ensure its generalization using various KPIs. - Improve network performance using regularization techniques such as dropout.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will develop, deploy, and run a TFX pipeline on Google Cloud Vertex Pipelines.

In this 1-hour long project-based course, you will learn how to Train SVM regression model- with large & small margin, second degree polynomial kernel, make prediction using Linear SVM classifier; how a small weight vector results in a large margin? and finally pictorial representation for Hinge loss. This project gives you easy access to the invaluable learning techniques used by experts in machine learning. Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your understanding to thoroughness in machine learning.

Have you ever wondered how SQL queries work in R? Have you ever thought about whether it is possible to use or write SQL queries in R? Then, you are in the right place. This project-based course Manipulate R data frames using SQL in RStudio is for people who are learning R and who may be well-versed in SQL or even for experienced R programmers who seek useful ways for data manipulation in R. It is for people who are interested in advancing their knowledge and skills in using SQL in R. In this project, we will write very nice queries to manipulate the gapminder and UCBAdmissions R data frames using the sqldf package in RStudio. This project is extremely important for you as an R and SQL user. You will understand how the SQL SELECT statement works to interact with R to get the desired result. We will start this hands-on project by installing and importing the required packages and data sets for this project. Be rest assured that you will learn a ton of good work here. By the end of this 2-hour-long project, you will be able to use SELECT statements together with the WHERE clause to set conditions on data retrieved from R data frames. Also, you will understand how to use the WHERE clause together with other SQL operators like AND, OR, IN, NOT IN, BETWEEN- AND, NOT BETWEEN- AND, and other comparison operators to retrieve data from the data frames. Going forward, we will consider how to use wildcard characters with the LIKE and NOT LIKE operators for pattern matching. By extension, we will learn how to create data summaries or aggregates using SQL aggregate functions. In this project, we will move systematically by first introducing the SELECT statements using simple examples. Then, we will write slightly complex queries to solve some SQL challenges. Therefore, to complete this project, it is required that you have prior experience with using SQL and R. I recommend that you should complete the projects titled: “Getting Started with R” and “Querying Databases using SQL SELECT statements” before you take this current project. These introductory projects in using SQL and R will provide every necessary foundation to complete this current project. However, if you are comfortable writing queries in SQL, please join me on this wonderful ride! Let’s get our hands dirty!

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more. By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career.

With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions. The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Data-driven technologies like AI, when designed with ethics in mind, benefit both the business and society at large. But it’s not enough to say you will “be ethical” and expect it to happen. We need tools and techniques to help us assess gaps in our ethical behaviors and to identify and stop threats to our ethical goals. We also need to know where and how to improve our ethical processes across development lifecycles. What we need is a way to manage ethical risk. This third course in the Certified Ethical Emerging Technologist (CEET) professional certificate is designed for learners seeking to detect and mitigate ethical risks in the design, development, and deployment of data-driven technologies. Students will learn the fundamentals of ethical risk analysis, sources of risk, and how to manage different types of risk. Throughout the course, learners will learn strategies for identifying and mitigating risks. This course is the third of five courses within the Certified Ethical Emerging Technologist (CEET) professional certificate. The preceding courses are titled Promote the Ethical Use of Data-Driven Technologies and Turn Ethical Frameworks into Actionable Steps.

Former Major League Baseball (MLB) player Matt Kata joins MathWorks to introduce you to data analysis using baseball statistics. By analyzing historic batting statistics, you will explore player careers and answer the question: When do great hitters peak in their career? In this project, you will work in MATLAB, a programming environment used by millions of engineers and scientists, and now MLB players! You’ll have access to pitching, batting, and defensive statistics dating back to 1871, enabling you to explore and answer a wide variety of questions. You will compute statistics like On-base Plus Slugging (OPS), visualize results, and filter data to highlight players that meet criteria you specify, such as the number of home runs. Whether you’re analyzing sports data, financial markets, or electric engine performance, you can apply the data analysis skills you learn in this project to many other fields and applications. So, step up to the plate and take a swing at MATLAB for data analysis.