Machine Learning Engineer for Microsoft Azure

Colleagues, the Machine Learning Engineer for Microsoft Azure program will strengthen your machine learning skills and build practical experience by training, validating, and evaluating models using Azure Machine Learning. Students will enhance their skills by building and deploying sophisticated machine learning solutions using popular open source tools and frameworks, and gain practical experience running complex machine learning tasks using the built-in Azure labs accessible inside the Udacity classroom. The three skill-based training modules with a hands-on project include: 1) Using Azure Machine Learning - Machine learning is a critical business operation for many organizations. Learn how to configure machine learning pipelines in Azure, identify use cases for Automated Machine Learning, and use the Azure ML SDK to design, create, and manage machine learning pipelines in Azure (Project - Optimizing an ML Pipeline in Azure)., 2) Machine Learning Operations - operationalizing machine learning, from selecting the appropriate targets for deploying models, to enabling Application Insights, identifying problems in logs, and harnessing the power of Azure’s Pipelines. All these concepts are part of core DevOps pillars that will allow you to demonstrate solid skills for shipping machine learning models into production (Project - Operationalizing Machine Learning), and 3) Capstone Project - use the knowledge you have obtained from this Nanodegree program to solve an interesting problem. You will have to use Azure’s Automated ML and HyperDrive to solve a task. Finally, you will have to deploy the model as a web service and test the model endpoint. Prior experience with Python, Machine Learning, and Statistics is recommended.

Sign-up today (teams & execs welcome): https://tinyurl.com/2pe8hrvj 

Much career success, Lawrence E. Wilson - Online Learning Central

Computer Vision (Training)

Colleagues, this Computer Vision training program equips you to write programs to analyze images, implement feature extraction, and recognize objects using deep learning models. Learn cutting-edge computer vision and deep learning techniques—from basic image processing, to building and customizing convolutional neural networks. Apply these concepts to vision tasks such as automatic image captioning and object tracking, and build a robust portfolio of computer vision projects. Master the computer vision skills behind advances in robotics and automation. Write programs to analyze images, implement feature extraction, and recognize objects using deep learning models. Training modules - each with a hands-on project cover: 1) Introduction to Computer Vision - Master computer vision and image processing essentials. Learn to extract important features from image data, and apply deep learning techniques to classification tasks (Project: Facial Keypoint Detection), 2) Facial Keypoint Detection - Advanced Computer Vision and Deep Learning, learn to apply deep learning architectures to computer vision tasks. Discover how to combine CNN and RNN networks to build an automatic image captioning application (Project: Automatic Image Captioning), and 3) Automatic Image Captioning - Object Tracking and Localization, learn how to locate an object and track it over time. These techniques are used in a variety of moving systems, such as self-driving car navigation and drone flight (Project: Landmark Detection and Tracking). Basic knowledge of Python, Statistics, Machine Learning and  Deep Learning is recommended.

Sign-up today (teams & execs welcome): https://tinyurl.com/3s9syf3r 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy 

How to Implement Search Algorithms with Python

Colleagues, the How to Implement Search Algorithms with Python program teaches you to optimize your approach for each search in your application and makes your overall system more efficient. Proficiency in this topic will help prepare you for your next coding interview and will help you understand how data retrieval methods work. Gain high-demand, marketable skills in Linear Search, 2) Finding Elements in Lists - Linear search can be used to search for a desired value in a list. It achieves this by examining each of the elements and comparing it with the search element starting with the first element, 3) Best Case Performance - Linear search is not considered the most efficient search algorithm, especially for lists of large magnitudes. However linear search is a great choice if you expect to find the target value, 4) Worst Case Performance - There are two worst cases for linear search. Case 1: when the target value at the end of the list, 5) Average Case Performance - If this search was used 1000 times on 1000 different lists, some of them would be the best case, some the worst. For most searches, it would be somewhere in between., 6) Time Complexity of Linear Search - Linear search runs in linear time. Its efficiency can be expressed as a linear function, with the number of comparisons to find a target increasing linearly as the size of the list, N, 7) Review - Congratulations! You have learned how linear search works and how to use it to search for values in lists. Let’s review what we learned: * Linear search is a search algorithm that is sequential, and 8) Linear & Binary Search Project - Learn to modify a version of binary search to look for data in a sparse dataset.

Enroll today (eams & execs welcome): https://fxo.co/Ccqx 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academ

Custom Models, Layers, and Loss Functions with TensorFlow

Colleagues, the Custom Models, Layers, and Loss Functions with TensorFlow program provide learners with more control over their model architecture and tools that help them create and train advanced ML models. Gain high-demand skills in Functional APIs, Custom Layers, Custom and Exotic Models with Functional APIs and Custom Loss Functions. Compare Functional and Sequential APIs, discover new models you can build with the Functional API, and build a model that produces multiple outputs including a Siamese network, Build custom loss functions (including the contrastive loss function used in a Siamese network) in order to measure how well a model is doing and help your neural network learn from training data, Build off of existing standard layers to create custom layers for your models, customize a network layer with a lambda layer, understand the differences between them, learn what makes up a custom layer, and explore activation functions, and Build off of existing models to add custom functionality, learn how to define your own custom class instead of using the Functional or Sequential APIs, build models that can be inherited from the TensorFlow Model class, and build a residual network (ResNet) through defining a custom model class. Training modules include: 1) Functional APIs - Compare how the Functional API differs from the Sequential API, and see how the Functional API gives you additional flexibility in designing models. Practice using the functional API and build a Siamese network, 2) Custom Loss Functions - measure how well a model is doing, and are used to help a neural network learn from the training data. Learn how to build custom loss functions, including the contrastive loss function that is used in a Siamese network, 3) Custom Layers - flexibility to implement models that use non-standard layers. Practice building off of existing standard layers to create custom layers for your models, 4) Custom Models - build off of existing models to add custom functionality. This week, extend the TensorFlow Model Class to build a ResNet model, and 5) Bonus Content - customize what your model outputs or how it behaves during training and implement a custom callback to stop training once the callback detects overfitting.

Enroll today (individuals & teams welcome): https://tinyurl.com/98u4wn2c 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy