This is a collection of free resources beyond the regularly shared books, MOOCs, and courses, mostly from over the past year. They start from zero and progress accordingly, and are suitable for individuals looking to pick up some of the basic ideas, before hopefully branching out further (see the final 2 resources listed below for more on that). These resources are not presented in any particular order, so feel free to pursue those which look most enticing to you. All credit goes the the individual authors of the respective materials, without whose hard work we would not have the benefit of learning from such great content. For many good reasons, much of the highest quality machine learning educational resources tend to have a very strong focus on theory, especially at the beginning.

When we encounter artificial intelligence in the media, it's often discussed at extremes. At one end, there are films, books, games and even news commentary that paint a picture of a world-ending intelligence. At the other end, people picture algorithms so powerful they can solve every major problem facing mankind. In reality, the capabilities for AI lie somewhere in between. For example, some of Elsevier's products use machine-learning driven image identification to better diagnose life-threatening illnesses – but these are tools are designed to aid the deductive work of human experts, not replace them.

Data management and data analytics are two critical fundamental resources that should be used by Thai enterprises and tech startups to develop innovative services backed by artificial intelligence (AI) technology, instead of working to develop intelligent products or services to compete with global players. Chai Wutiwiwatchai, research unit director of the National Electronics and Computer Technology Center (Nectec), said local enterprises can benefit from the many data sets held by state agencies through 20 ministries and the private sector. "Intelligent products and services driven by AI may not be easy to enter for local enterprises and startups, as there are too many global tech players and AI tech-embedded tools available for free in the market," he said. But the government must urgently digitise the existing data sets of all agencies, 70% of which are stored on paper and in portable document format (PDF) files. Speaking on the sidelines of the "AI Shapes the Future" forum last week, Mr Chai said innovative products and services embedded with AI tech have been increasingly accessible in the global market for four years, especially through popular use cases of image recognition, biometrics, cybersecurity and smart speakers.

Statistics and machine learning are two very closely related fields. In fact, the line between the two can be very fuzzy at times. Nevertheless, there are methods that clearly belong to the field of statistics that are not only useful, but invaluable when working on a machine learning project. It would be fair to say that statistical methods are required to effectively work through a machine learning predictive modeling project. In this post, you will discover specific examples of statistical methods that are useful and required at key steps in a predictive modeling problem.

Deep Learning A-Z: Hands-On Artificial Neural Networks by Kirill Eremenko and Hadelin de Ponteves will teach you Deep Learning with Artificial Neural Networks. You will work with Tensorflow and Pytorch to build several different types of Neural Networks. Data Science: Deep Learning in Python by Lazy Programmer Inc. will teach you build Neural Networks from scratch in Python, numpy & TensorFlow. You will learn about the various types and terms associated to neural networks. Natural Language Processing with Deep Learning in Python by Lazy Programmer Inc. will teach you everything about deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets.

This week in Vienna kicks off an incredibly important global discussion happening at Electrify Europe; energy, electricity, and the transformation of our entire power structure. Have you thought about how we will power 100 billion connected things, as well as, support all the electric vehicles set to disrupt the combustion engine automotive industry? In an electricity sector undergoing rapid change and transition, it's vital for us to wrap our minds around the implications on the industry as a whole. Most of us are keenly aware of the conversations happening around Artificial Intelligence, Machine Learning, Blockchain, etc,, but I find it interesting that what powers our future is energy and I'm not hearing much discussion at the global events I have been keynoting this year on what's going to keep the lights on. That's when I found, Electrify Europe, a conference dedicated to bringing together thousands of innovators and thought leaders to discuss how the latest technologies will affect us, and how we can all benefit from evolving our businesses to position them for success in the future.

We propose a novel online algorithm for training deep feedforward neural networks that employs alternating minimization (block-coordinate descent) between the weights and activation variables. It extends off-line alternating minimization approaches to online, continual learning, and improves over stochastic gradient descent (SGD) with backpropagation in several ways: it avoids the vanishing gradient issue, it allows for non-differentiable nonlinearities, and it permits parallel weight updates across the layers. Unlike SGD, our approach employs co-activation memory inspired by the online sparse coding algorithm of [Mairal et al, 2009]. Furthermore, local iterative optimization with explicit activation updates is a potentially more biologically plausible learning mechanism than backpropagation. We provide theoretical convergence analysis and promising empirical results on several datasets.

This article was written by Monica Rogati. Monica is an independent data science executive and advisor. She built key data products and teams at Jawbone and LinkedIn; she now helps companies make the most out of their data. Do a project you care about. Make it good and share it. A quick search yields a plethora of possible resources that could help -- MOOCs, blogs, Quora answers to this exact question, books, Master's programs, bootcamps, self-directed curricula, articles, forums and podcasts.

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for IT professionals and data-scientists. The scikit-learn library is one of the most popular platforms for everyday Machine Learning and data science because it is built upon Python, a fully featured programming language. This comprehensive 2-in-1 course is a comprehensive, practical guide to master the basics and learn from real-life applications of machine learning. Learn how to build and evaluate the performance of efficient models using scikit-learn. This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

Increasing retention by just 10 percent will boost your business value by more than 30 percent. And AI is what can take you there, helping you anticipate customer needs, create personalized campaigns, identify high-value customers and more. Learn how to do it with marketing automation platforms powered by AI when you join this VB Live event! "Mobile marketers are generally using only five percent of their available data," says Kara Dake, VP of Growth and Partnerships at CleverTap. "You're gathering a ton of information, but it's a challenge, knowing what to do with all that data, and so many data points."