In this 1-hour long project-based course, you will learn how to test and debug your code in Visual Studio Code. You will find the bugs in a sample program and fix them. Topics covered include: building code for debugging, breakpoints, watch, call stack, conditional breakpoints, function breakpoints, and debug console. Note: This course uses a C++ program as a learning tool. Basic knowledge of C++ is useful but any coding experience of a modern and similar language (such as C, Swift, Java, JavaScript, Kotlin) will suffice.

This course is for business analysts and SAS programmers who want to learn data manipulation techniques using the SAS DATA step and procedures to access, transform, and summarize data. The course builds on the concepts that are presented in the Getting Started with SAS Programming course and is not recommended for beginning SAS software users. In this course you learn how to understand and control DATA step processing, create an accumulating column and process data in groups, manipulate data with functions, convert column type, create custom formats, concatenate and merge tables, process repetitive code, and restructure tables. This course addresses Base SAS software. Before attending this course, you should be able to write DATA step code to access data, subset rows and columns, compute new columns, and process data conditionally. You should also be able to sort tables using the SORT procedure and apply SAS formats.

In this 2-hour long project-based course, you will learn how to implement a Triplet Loss function, create a Siamese Network, and train the network with the Triplet Loss function. With this training process, the network will learn to produce Embedding of different classes from a given dataset in a way that Embedding of examples from different classes will start to move away from each other in the vector space. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should be familiar with Python, Keras, Neural Networks. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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 use object-oriented programming concepts, design and implement a class based on the project needs and how to run and test the app that you implemented 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.

We will learn computational methods -- algorithms and data structures -- for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used. We will use Python to implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets.

In this one-hour guided project, you will learn how to process geospatial data using Python. We will go through different geoprocessing tasks including how to create Geodataframes from CSV files and perform a spatial join.

In this course, we will explore fundamental issues of fairness and bias in machine learning. As predictive models begin making important decisions, from college admission to loan decisions, it becomes paramount to keep models from making unfair predictions. From human bias to dataset awareness, we will explore many aspects of building more ethical models.

This course can also be taken for academic credit as ECEA 5731, part of CU Boulder’s Master of Science in Electrical Engineering degree. In this course, you will learn the purpose of each component in an equivalent-circuit model of a lithium-ion battery cell, how to determine their parameter values from lab-test data, and how to use them to simulate cell behaviors under different load profiles. By the end of the course, you will be able to: - State the purpose for each component in an equivalent-circuit model - Compute approximate parameter values for a circuit model using data from a simple lab test - Determine coulombic efficiency of a cell from lab-test data - Use provided Octave/MATLAB script to compute open-circuit-voltage relationship for a cell from lab-test data - Use provided Octave/MATLAB script to compute optimized values for dynamic parameters in model - Simulate an electric vehicle to yield estimates of range and to specify drivetrain components - Simulate battery packs to understand and predict behaviors when there is cell-to-cell variation in parameter values

In this 1.5 hour long project-based course, you will learn about constants and variables in TensorFlow, you will learn how to use automatic differentiation, and you will apply automatic differentiation to solve a linear regression problem. By the end of this project, you will have a good understanding of how machine learning algorithms can be implemented in TensorFlow. In order to be successful in this project, you should be familiar with Python, Gradient Descent, Linear Regression. 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.

Welcome to this Guided Project on Creating Your First Python Program, From UST. For more than 20 years, UST has worked side by side with the world’s best companies to make a real impact through transformation. Powered by technology, inspired by people and led by their purpose, they partner with clients from design to operation. With this Guided Project from UST, you can quickly build in-demand job skills and expand your career opportunities in the Computer Science field by learning the foundational elements of the Python programming language and its basic syntax. Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis. Python is a general purpose language, meaning it can be used to create a variety of different programs and isn’t specialized for any specific problems. This versatility, along with its beginner-friendliness, has made it one of the most-used programming languages today. Through hands-on, practical experience, you will be guided through concepts that Python Programmers use every day to perform their job duties, like using the Terminal and a Text Editor. Together, we will explore how to use variables, create functions, lists and conditional statements, as well as utilize For and While loops. You will then apply the concepts to create your first command line application which will manage a To-Do List. This project is great for learners who are looking to get started with Python programming, and do not have any prior programming experience. By the end of this Guided Project, you should feel more confident about working with the Python language, understanding what it is used for, and have confirmed your skills by creating your own to-do list, which can be used for showing an employer or making your routine daily tasks more efficient through automation. Ready to become a Programmer? Start learning Python today!