In this capstone project, you will leverage what you've learned throughout the Nanodegree program to solve a problem of your choice by applying machine learning algorithms and techniques. You will first define the problem you want to solve and investigate potential solutions and performance metrics. Next, you will analyze the problem through visualizations and data exploration to have a better understanding of what algorithms and features are appropriate for solving it. You will then implement your algorithms and metrics of choice, documenting the preprocessing, refinement, and postprocessing steps along the way. Afterwards, you will collect results about the performance of the models used, visualize significant quantities, and validate/justify these values.
The use of artificial intelligence and the "next-generation" of virtual learning environments (VLEs) are two areas of technology that have been forecast to have a major impact on higher education in the future, according to the expert panel of a major new report. The NMC Horizon Report: 2017 Higher Education Edition is produced by the New Media Consortium – a community of hundreds of universities, colleges, museums and research organisations driving innovation across their campuses – and is the flagship publication of the NMC Horizon Project, which analyses emerging technology uptake in education. Artificial intelligence, the report notes, has the "potential to enhance online learning, adaptive learning software, and research processes in ways that more intuitively respond to and engage with students". Samantha Adams Becker, senior director of publications and communications at NMC and the report's editor, said that the higher education world was already seeing the initial benefits of AI, which was "very much driving" the adaptive learning field.
Everyone wants to minimize losses and maximize profits. AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Thanks to Deep Learning and improved methodologies to analyze data, Data Analysts and Data Scientists are increasingly using data to make informed decisions. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It's something that's moving beyond the realm of data science – if you're a developer, this course gives you a great opportunity to expand your skillset.
About this course: You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience.
This course provides you to be able to build Deep Neural Networks models for different business domains with one of the most common machine learning library TensorFlow provided by Google AI team. The both concept of deep learning and its applications will be mentioned in this course. Also, we will focus on Keras. Also, you don't have to be attend any ML course before.