Get yourself trained on Practical Machine Learning with this Online Training Practical Machine Learning and Deep Learning with TensorFlow.
Online Training Practical Machine Learning and Deep Learning with TensorFlow
Video Learning Path OverviewA Learning Path is a specially tailored course that brings together two or more different topics that lead you to achieve an end goal. Much thought goes into the selection of the assets for a Learning Path, and this is done through a complete understanding of the requirements to achieve a goal.Machine learning and Deep Learning have been gaining immense traction lately, and TensorFlow is quickly becoming the technology of choice for machine learning and deep learning, because of its ease to develop intelligent machine learning applications and powerful neural networks.This well thought out Learning Path takes a step by step approach to teach you how to use TensorFlow for performing machine learning and deep learning on a day-to-day basis. Beginning with an introduction to machine learning with TensorFlow, you will learn how to create machine learning models, neural networks, and deep learning models with practical projects.Once you are comfortable in creating models and neural networks with TensorFlow, you will learn to practically apply them to create your own machine learning systems or to solve common machine learning problems such as inaccuracy or duplication of data.Moving further, the main challenge with deep learning models and applications is to find the right way to deploy them within the companys IT infrastructure. This is where the serverless framework comes in. You will learn to use serverless framework for deploying your applications on a cloud computing platform such as AWS Lambda. Going serverless with AWS Lambda enables you to separate your application into many small independent services. Thus, increasing your product flexibility and reducing your costs.Key FeaturesLeverage the essential concepts of deep learning and machine learning with TensorFlowBuild real-world projects with predictive models, classification, and anomaly detection algorithmsGain practical experience by working hands-on with serverless infrastructure such as AWS LambdaStart building deep learning APIs, followed by mastering processing pipelines and deployment pipelinesAuthor BiosKaiser Hamid Rabbi is an aspiring Data Scientist who is super-passionate about Artificial Intelligence, Machine Learning, and Data Science. Over the last four years, he has entirely devoted himself to learning more about data science technologies such as Python, Machine Learning, Deep Learning, Artificial Intelligence, data mining, data analysis, recommender systems, and so on. Kaiser also has a huge interest in Lygometry (things we know we do not know!) and always tries to understand Domain Knowledge from his projects as far as possible.Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, machine learning, and cloud computing. Over the past few years, they have worked with some of the world’s largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the World’s most popular soft drinks companies, helping each of them to make better sense of its data and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action. Tom Joy is studying for a PhD at the University of Oxford in the field of Semantic SLAM, which is the process of simultaneously localizing a robot in space; producing a map/understanding of the surrounding area whilst also detecting and delineating objects in 3D space. Achieving this requires a high level of competency in computer vision, machine learning, and optimization. Tom has extensive experience in computer vision and machine learning, having taken several internships and placements over the course of his degree and spent time in industry prior to starting his PhD. He is a big advocate of explaining concepts simply and in a clear and concise manner; he strives to obtain and provide a comprehensive understanding of all relevant methods to the task at hand.Mohamed Elsayed Mohamed Elhaj Abdou is a Junior Machine Learning and Software Engineer, specializing in Image Processing and Computer Vision applications. He has 4 years’ research experience in Localization, and research interests in SLAM, Building 3D environments and localizing a robot for indoor and outdoor use involving Computer Vision, Machine Learning, and Deep Learning. He also has 5 years’ experience in designing and mentoring different projects in international competitions such as ROVs, minesweeper, and Quadcopters.Rustem Feyzkhanov is a machine learning engineer at Instrumental and works on creating analytical models for manufacturing industry. He is also passionate about serverless infrastructures and AI deployment on them. He has ported several packages on AWS Lambda, ranging from TensorFlow/Keras/sklearn for ML to PhantomJS/Selenium/WRK for web scraping. One of these apps was featured on the AWS serverless repo home page.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.