The list below is a small selection from Open Culture. We picked up classes relevant to data scientists, and removed links that no longer work at the time of writing. If you know of any other interesting courses, email me and we will include them if they are relevant.

Machine learning is the predictive heart of big data analytics, and one of the key skills that separates data scientists from mere analysts. But getting started with machine learning can be a challenge. Here are a few ways beginners can get off the ground with their machine learning adventure. Machine learning is a vast field with many different specialties, so it's quite easy for a beginner to get overwhelmed. For instance, one specialty called deep learning powers many of today's artificial intelligence breakthroughs.

Why do 97% of marketing influencers believe the future of digital marketing will involve human marketers working with machine learning-powered automation? Thought leaders in PPC, social and mobile marketing explain in this in-depth survey. Are disciplines such as search engine marketing, social and mobile marketing all trending towards a fully automated world where artificial intelligence (AI) robots take over our jobs? In a survey of top influencers in online marketing with expertise in paid search, social and mobile, 97% of respondents suggested that the future of marketing will actually be smart marketers working hand-in-hand with machine learning-based automation solutions. To help us understand the growing role of machine learning in marketing, we spoke with some of the top influencers in the space, including Michael Brenner (@brennermichael) of Marketing Insider Group, Serena Ehrlich (@serena) of BusinessWire, Adelyn Zhou (@adelynzhou) of TOPBOTS and Chris Messina (@chrismessina), creator of the hashtag - among others. Selected responses to the survey are below. Interested in learning more about how new technology and academic disciplines such as machine learning and data science are changing digital forever?

Recommended online course: If you like video courses, I'd recommend the following inexpensive Udemy course on neural networks: Deep Learning A-Z: Hands-On Artificial Neural Networks Here's an outline of the tutorial, with links, so you can easily navigate to the parts you want: Artificial neural networks (ANNs) are software implementations of the neuronal structure of our brains. In a supervised ANN, the network is trained by providing matched input and output data samples, with the intention of getting the ANN to provide a desired output for a given input. As mentioned previously, biological neurons are connected hierarchical networks, with the outputs of some neurons being the inputs to others. These structures can come in a myriad of different forms, but the most common simple neural network structure consists of an input layer, a hidden layer and an output layer.

Recommended online course: If you like video courses, I'd recommend the following inexpensive Udemy course on neural networks: Deep Learning A-Z: Hands-On Artificial Neural Networks Here's an outline of the tutorial, with links, so you can easily navigate to the parts you want: Artificial neural networks (ANNs) are software implementations of the neuronal structure of our brains. In a supervised ANN, the network is trained by providing matched input and output data samples, with the intention of getting the ANN to provide a desired output for a given input. As mentioned previously, biological neurons are connected hierarchical networks, with the outputs of some neurons being the inputs to others. These structures can come in a myriad of different forms, but the most common simple neural network structure consists of an input layer, a hidden layer and an output layer.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments. New posts will be published here first, and previous posts are archived on the Udacity blog.

The World Association of Girl Guides and Girl Scouts is updating its image with a number of new achievement badges aimed at encouraging young women into science and technology. My memories of the Girl Guides involve marshmallow toasting, tying knots and being assessed on my table-laying skills for a badge no doubt long-consigned to the archives. Fast forward some 25 years and it's clear much as changed. In an international organisation that liaises with Google and Microsoft among others, today's young guides are just as likely to be gathered round an engineering bench as a campfire. Sixteen-year-old Catherine Young is on a mission to boost girls' interest in engineering and has found the Girl Guides a valuable platform for her campaign.

This training is ideal for engineers creating algorithms and software for visual machine perception in all types of applications (e.g. in the industrial, medical, consumer, retail, public safety or automotive area) who want to quickly come up to speed on using TensorFlow for these applications. It's also appropriate for managers who want to get a flavor for creating deep neural networks and using TensorFlow. After the training, attendees will be ready to begin using TensorFlow productively in their work. In addition, for attendees who require an introduction to deep neural network algorithms, the Embedded Vision Alliance will offer a two-hour video tutorial presentation online prior to the TensorFlow class.

Most of us regard self-driving cars, voice assistants, and other artificially intelligent technologies as revolutionary. For the next generation, however, these wonders will have always existed. AI for them will be more than a tool; in many cases, AI will be their co-worker and a ubiquitous part of their lives. If the next generation is to use AI and big data effectively – if they're to understand their inherent limitations, and build even better platforms and intelligent systems -- we need to prepare them now. That will mean some adjustments in elementary education and some major, long-overdue upgrades in computer science instruction at the secondary level.

Work, job, play, privacy, communication, finance, war, and dating: algorithms and the machines that run them have upended them all. Will artificial intelligence become as ubiquitous as electricity? Is there any industry AI won't touch? Will AI tend to steal jobs and exacerbate income inequalities, or create new jobs and amplify human abilities at work -- or, both? How can the global population adjust to the changes ushered in by artificial intelligence and its capabilities?