By the end of this project, you will be able to create The Classic Ping Pong game using Python and Turtle graphics. You’ll also be able to identify and use most of Turtle’s modules and functions that helps you develop and build your own game. Moreover, you’ll be able to edit and manipulate the objects created by Turtle however you like. Turtle graphics is a pre-installed Python library that’s a trendy way of introducing programming to beginners. It helps visualize what programming can do. It’s a straightforward yet versatile way to understand the concepts of Python. This guided project is for beginner-intermediate programmers who already have a general knowledge of Python basics and want to test out their knowledge with a real application and looking forward to developing their very first game in less than 1 hour. This project can be your portal into game development. Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings. All the features of this course are available for free. It does not offer a certificate upon completion.

In previous courses in the Specialization, we have discussed how to sequence and compare genomes. This course will cover advanced topics in finding mutations lurking within DNA and proteins. In the first half of the course, we would like to ask how an individual's genome differs from the "reference genome" of the species. Our goal is to take small fragments of DNA from the individual and "map" them to the reference genome. We will see that the combinatorial pattern matching algorithms solving this problem are elegant and extremely efficient, requiring a surprisingly small amount of runtime and memory. In the second half of the course, we will learn how to identify the function of a protein even if it has been bombarded by so many mutations compared to similar proteins with known functions that it has become barely recognizable. This is the case, for example, in HIV studies, since the virus often mutates so quickly that researchers can struggle to study it. The approach we will use is based on a powerful machine learning tool called a hidden Markov model. Finally, you will learn how to apply popular bioinformatics software tools applying hidden Markov models to compare a protein against a related family of proteins.

In this 1.5-hour long project-based course, you will learn how to apply image data augmentation in Keras. We are going to focus on using the ImageDataGenerator class from Keras’ image preprocessing package, and will take a look at a variety of options available in this class for data augmentation and data normalization. Since this is a practical, project-based course, you will need to prior experience with Python programming, convolutional neural networks, and Keras with a TensorFlow backend. Data augmentation is a technique used to create more examples, artificially, from an existing dataset. This is useful if your dataset is small and you want to increase the number of examples. Data augmentation can often solve over-fitting so that your model generalizes well after training. For images, a variety of augmentation can be applied to increase the number of examples. 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.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will use an AI Platform Notebook instance to retrieve as many transactions as possible from BigQuery within 2 degrees of separation from the pizza exchange, post-process the transactions to remove excess transaction, then visualize the directed graph.

In this 1-hour long project-based course, you will discover optimal situations to use fundamental data structures such as Arrays, Stacks, Queues, Hashtables, LinkedLists, and ArrayLists. By the end of this project you will create an application that processes an UN dataset, and manipulates this dataset using a variety of different data structures. In addition, you will explore how to implement each data structure using industry-standard Java practices, and gain experience manipulating real life data sets. Data structures are an essential tool for any developer, and allow us to store and efficiently access data for even large datasets. Mastery of data structures allows your programs to be scalable and function without taking up too many system resources. We will use the Java Collections versions of each of these data structures, just as you would in real-life. Students can expect to walk away from the course confident in their ability to manipulate essential Java data structures, and have a working knowledge theory behind each data structure. 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 1.5 hour long project-based course, you will learn to create and train a Convolutional Neural Network (CNN) with an existing CNN model architecture, and its pre-trained weights. We will use the MobileNet model architecture along with its weights trained on the popular ImageNet dataset. By using a model with pre-trained weights, and then training just the last layers on a new dataset, we can drastically reduce the training time required to fit the model to the new data . The pre-trained model has already learned to recognize thousands on simple and complex image features, and we are using its output as the input to the last layers that we are training. In order to be successful in this project, you should be familiar with Python, Neural Networks, and CNNs. 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.

The job market for architects, engineers, and analytics professionals with Big Data expertise continues to increase. The Academy’s Big Data Career path focuses on the fundamental tools and techniques needed to pursue a career in Big Data. This course includes: data processing with python, writing and reading SQL queries, transmitting data with MaxCompute, analyzing data with Quick BI, using Hive, Hadoop, and spark on E-MapReduce, and how to visualize data with data dashboards. Work through our course material, learn different aspects of the Big Data field, and get certified as a Big Data Professional!

This course focuses on the recovery of the 3D structure of a scene from its 2D images. In particular, we are interested in the 3D reconstruction of a rigid scene from images taken by a stationary camera (same viewpoint). This problem is interesting as we want the multiple images of the scene to capture complementary information despite the fact that the scene is rigid and the camera is fixed. To this end, we explore several ways of capturing images where each image provides additional information about the scene. In order to estimate scene properties (depth, surface orientation, material properties, etc.) we first define several important radiometric concepts, such as, light source intensity, surface illumination, surface brightness, image brightness and surface reflectance. Then, we tackle the challenging problem of shape from shading - recovering the shape of a surface from its shading in a single image. Next, we show that if multiple images of a scene of known reflectance are taken while changing the illumination direction, the surface normal at each scene point can be computed. This method, called photometric stereo, provides a dense surface normal map that can be integrated to obtain surface shape. Next, we discuss depth from defocus, which uses the limited depth of field of the camera to estimate scene structure. From a small number of images taken by changing the focus setting of the lens, a dense depth of the scene is recovered. Finally, we present a suite of techniques that use active illumination (the projection of light patterns onto the scene) to get precise 3D reconstructions of the scene. These active illumination methods are the workhorse of factory automation. They are used on manufacturing lines to assemble products and inspect their visual quality. They are also extensively used in other domains such as driverless cars, robotics, surveillance, medical imaging and special effects in movies.

This course can also be taken for academic credit as ECEA 5734, part of CU Boulder’s Master of Science in Electrical Engineering degree. In this course, you will learn how to design balancing systems and to compute remaining energy and available power for a battery pack. By the end of the course, you will be able to: - Evaluate different design choices for cell balancing and articulate their relative merits - Design component values for a simple passive balancing circuit - Use provided Octave/MATLAB simulation tools to evaluate how quickly a battery pack must be balanced - Compute remaining energy and available power using a simple cell model - Use provided Octave/MATLAB script to compute available power using a comprehensive equivalent-circuit cell model