In this 1 hr 40 mins long project-based course, you will learn about the process of developing a new product for start-up companies, and small and medium-sized enterprises (SMEs). You will learn about idea generation and the evaluation processes in product development by using an idea generation model and online resources like Google Trends and Amazon. You will use methods to evaluate your product concept through market segmentation, growth potential, and the competition to your product. You will also evaluate a supplier and the cost to your product by analyzing component prices and production rates. By the end of this project, you will be able to create a full retrospective plan for the product launch and understand how and why the specifications are done. 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 course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.

This guided project course is part of the "Tensorflow for Convolutional Neural Networks" series, and this series presents material that builds on the second course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow. In this 2-hour long project-based course, you will learn the fundamentals of CNNs, structure, components, and how they work, and you will learn practically how to solve an image classification deep learning task in the real world and create, train, and test a neural network with Tensorflow using real-world images, and you will get a bonus deep learning exercise implemented with Tensorflow. By the end of this project, you will have learned the fundamentals of convolutional neural networks and created a deep learning model with TensorFlow on a real-world dataset. This class is for learners who want to learn how to work with convolutional neural networks and use Python for building convolutional neural networks with TensorFlow, and for learners who are currently taking a basic deep learning course or have already finished a deep learning course and are searching for a practical deep learning project with TensorFlow. Also, this project provides learners with further knowledge about creating and training convolutional neural networks and improves their skills in Tensorflow which helps them in fulfilling their career goals by adding this project to their portfolios.

In this 1-hour long guided project, you will learn how to use the "What-If" Tool (WIT) in the context of training and testing machine learning prediction models. In particular, you will learn a) how to set up a machine learning application in Python by using interactive Python notebook(s) on Google's Colab(oratory) environment, a.k.a. "zero configuration" environment, b) import and prepare the data, c) train and test classifiers as prediction models, d) analyze the behavior of the trained prediction models by using WIT for specific data points (individual basis), e) moving on to the analysis of the behavior of the trained prediction models by using WIT global basis, i.e., all test data considered. 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 2-hour project-based course, you will be able to : - Understand the Segmentation Dataset and you will write a custom dataset class for Image-mask dataset. Additionally, you will apply segmentation augmentation to augment images as well as its masks. For image-mask augmentation you will use albumentation library. You will plot the image-Mask pair. - Load a pretrained state of the art convolutional neural network for segmentation problem(for e.g, Unet) using segmentation model pytorch library. - Create train function and evaluator function which will helpful to write training loop. Moreover, you will use training loop to train the model.

In this 1 hour long project based course, you will learn to save, load and restore models with Keras. In Keras, we can save just the model weights, or we can save weights along with the entire model architecture. We can also export the models to TensorFlow's Saved Mode format which is very useful when serving a model in production, and we can load models from the Saved Model format back in Keras as well. In order to be successful in this project, you should be familiar with python programming, and basics of 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.

For a given demand profile for 8 products over a 9 week period, we determine the optimal production plan for minimal inventory. "Mixed Integer Linear Programming" method is applied using R lpSolve library.

In this short, 1 hour long guided project, we will use a Convolutional Neural Network - the popular VGG16 model, and we will visualize various filters from different layers of the CNN. We will do this by using gradient ascent to visualize images that maximally activate specific filters from different layers of the model. We will be using TensorFlow as our machine learning framework. The project uses the Google Colab environment which is a fantastic tool for creating and running Jupyter Notebooks in the cloud, and Colab even provides free GPUs for your notebooks. You will need prior programming experience in Python. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, Convolutional Neural Networks, and optimization algorithms like gradient descent but want to understand how to use the TensorFlow to visualize various filters of a CNN. 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.

By the end of this project, you will learn how to Analyze a real network graph using python. you will learn how to use Networkx module to Visualize a graph and to calculate some important measures which can describe characteristics of our graph. you will also learn About Centrality measures to find Important nodes in a graph. In the final task of the project we are going talk about Scale-free networks and we are going to prove that Facebook Network graph has familiarities with Scale-free networks.

What is a certification? How is it different than a certificate or credential? This mini-course will answer these questions and provide learners direction on how to prepare for a certification exam from CertNexus or an other certification vendor. It includes tips and tricks to succeed in your journey towards certification, as well as step by step instructions how to schedule and take your exam, whether in person or online. In addition we will provide next steps after your certification, including posting your badge to social posts and your organization. Candidates with industry recognized certifications can earn up to 25% more than candidates without a certification. Learn how to successfully prepare for, pass, and share your certification.