With continual advancements in artificial intelligence, it won't be long before machine learning touches more and more aspects of our lives – including the way we drive. As more autonomous cars hit the road, the way we can expect to drive in the future is shifting. In this course, you'll learn the robotics fundamentals and machine learning concepts that go into self-driving cars. The course will help you develop an understanding of artificial neural networks, and some of the complex algorithms that are applied in these vehicles. Increase your knowledge about machine learning with The Robotics & Machine Learning of Self-Driving Cars.

Long Short-Term Memory (LSTM) Recurrent Neural Networks are a powerful type of deep learning suited for sequence prediction problems. A possible concern when using LSTMs is if the added complexity of the model is improving the skill of your model or is in fact resulting in lower skill than simpler models. In this post, you will discover simple experiments you can run to ensure you are getting the most out of LSTMs on your sequence prediction problem. Get the Most out of LSTMs on Your Sequence Prediction Problem Photo by DoD News, some rights reserved. The LSTM recurrent neural network has a few key capabilities that give the method its impressive power on a wide range of sequence prediction problems.

As artificial intelligence alters work done in all manner of industries, companies and governments can help workers transition by supporting incomes and facilitating skills training. The pressure is on for companies and governments to address the ways that artificial intelligence (AI) is altering the future of work. In this video, recorded at the Aspen Ideas Festival in June, experts--Markle Foundation CEO and president Zoë Baird; Joy Buolamwini, founder of the Algorithmic Justice League at MIT Media Lab; James Fallows, national correspondent of the Atlantic; and Coursera cofounder Andrew Ng--discuss how to make the transition into this new age easier for everyone. Andrew Ng: AI is the new electricity. About 100 years ago, we started rolling out electricity in the United States, and it changed every single major industry, everything ranging from healthcare and culture to transportation, communications, and manufacturing are now all electricity powered.

About this course: If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description.

The Keras library provides a way to calculate and report on a suite of standard metrics when training deep learning models. In addition to offering standard metrics for classification and regression problems, Keras also allows you to define and report on your own custom metrics when training deep learning models. This is particularly useful if you want to keep track of a performance measure that better captures the skill of your model during training. In this tutorial, you will discover how to use the built-in metrics and how to define and use your own metrics when training deep learning models in Keras. Metrics and How to Use Custom Metrics for Deep Learning with Keras in Python Photo by Indi Samarajiva, some rights reserved.

Churn prediction is big business. It minimizes customer defection by predicting which customers are likely to cancel a subscription to a service. Though originally used within the telecommunications industry, it has become common practice across banks, ISPs, insurance firms, and other verticals. The prediction process is heavily data-driven and often utilizes advanced machine learning techniques. In this post, we'll take a look at what types of customer data are typically used, do some preliminary analysis of the data, and generate churn prediction models -- all with Spark and its machine learning frameworks.

Say Machine Learning to someone, and if they recognize the term, they'll probably think, "tech company." But while the origin stories of transformative technologies like machine learning, deep learning, and artificial intelligence often seem to take root in Silicon Valley, the truth is these are industry-agnostic innovations. Their impact is being felt across countless fields you might never have thought of as being ripe for technological advancement. Think about it like this: If you were a farmer, and someone came to you and said, there's a technology out there that can accurately predict your crop yields, would you be interested? Well, this is exactly what Descartes Labs does.

I have been working on three new AI projects, and am thrilled to announce the first one: deeplearning.ai, These courses will help you master Deep Learning, apply it effectively, and build a career in AI. Just as electricity transformed every major industry starting about 100 years ago, AI is now poised to do the same. Several large tech companies have built AI divisions, and started transforming themselves with AI. But in the next few years, companies of all sizes and across all industries will realize that they too must be part of this AI-powered future.

But you don't know where to start, or perhaps you have read some theory, but don't know how to implement what you have learned. This tutorial will help you break the ice, and walk you through the complete process from importing and analysing a dataset to implementing and training a few different well known classification algorithms and assessing their performance. I'll be using a minimal amount of discrete mathematics, and aim to express details using intuition, and concrete examples instead of dense mathematical formulas. You can read why here. We will be classifying flower-species based on their sepal and petal characteristics using the Iris flower dataset which you can download from Kaggle here. Kaggle, if you haven't heard of it, has a ton of cool open datasets, and is a place where data scientists share their work which can be a valuable resource when learning.

A small collection of introductory scikit-learn and Python stack tutorials for those with an existing understanding of machine learning looking to jump right into using a new set of tools.