In my last few articles, I've written about data science and machine learning. In case my enthusiasm wasn't obvious from my writing, let me say it plainly: it has been a long time since I last encountered a technology that was so poised to revolutionize the world in which we live. Think about it: you can download, install and use open-source data science libraries, for free. You can download rich data sets on nearly every possible topic you can imagine, for free. You can analyze that data, publish it on a blog, and get reactions from governments and companies.

Artificial Intelligence/Machine Learning field is getting a lot of attention right now, and knowing where to start can be a little difficult. I've been dabbling in this field, so I thought of curating the best resources in one place. All of these are curated based on if it's an inspiring read or a valuable resource. I hope this curated list help you get started on what you need to know about AI/Machine Learning on a technical level. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.

The workshop will walk the attendees through the Machine Learning process, how to build one in Azure ML Studio and publish the predictive experiment. With the enhancement of the R and Python code, participants will have the opportunity to customise the flow. Finally, the session will provide the ways of productionising the built solution by publishing it to Azure.

Are you frustrated with your sales results, even though you have put a lot of time, money and effort, your heart and soul into growing your revenue? Looking to grow your sales, quickly and effectively, but also prevent buyer's remorse and keep your clients loyal? This webinar will help you identify new ways (that you can start using right away!) to find, engage, win and keep new clients. Here's what you will discover: 1.FIND: How to find new clients who are in the market today 2.ENGAGE: How to connect with them instantly and engage into a dialogue 3.WIN: How to remove any resistance and win new clients 4.KEEP: How to prevent buyer's remorse and keep your clients loyal

Recently rebooted GigaOm has started a new and excellent podcast Voices in AI, where host Byron Reese, @byronreese, himself a promiment author and speaker, talks with some of the leading minds in AI, Machine Learning, and Data Science. I have only had time to listen to 2 episodes so far and both have been really excellent - I cannot recommend this podcast series highly enough. A bunch of episodes have already been released and here are the first 6 episodes which should keep you glued to your headphones! Episode 1: A Conversation with Yoshua Bengio In this episode, Byron and Yoshua talk about knowledge, unsupervised learning, how the brain learns, creativity, and machine translation. Episode 5: A Conversation with Daphne Koller In this episode, Byron and Daphne talk about consciousness, personalized medicine, and transfer learning.

One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. The course will teach you those fundamental concepts by implementing practical exercises which are based on live examples.

Text classification describes a general class of problems such as predicting the sentiment of tweets and movie reviews, as well as classifying email as spam or not. Deep learning methods are proving very good at text classification, achieving state-of-the-art results on a suite of standard academic benchmark problems. In this post, you will discover some best practices to consider when developing deep learning models for text classification. Best Practices for Document Classification with Deep Learning Photo by storebukkebruse, some rights reserved. The modus operandi for text classification involves the use of a word embedding for representing words and a Convolutional Neural Network (CNN) for learning how to discriminate documents on classification problems.

For a long time I've been looking for a good tutorial on implementing LSTM networks. They seemed to be complicated and I've never done anything with them before. Quick googling didn't help, as all I've found were some slides. Fortunately, I took part in Kaggle EEG Competition and thought that it might be fun to use LSTMs and finally learn how they work. I based my solution and this post's code on char-rnn by Andrej Karpathy, which I highly recommend you to check out.

In this post, we'll be doing a step-by-step walkthrough of a basic machine learning project, geared toward people with some knowledge of programming (preferably Python), but who don't have much experience with machine learning. By the end of this post, you'll understand what machine learning is, how it can help you, and be able to build your own machine learning classifiers for any dataset you want. We'll teach a computer how to distinguish between "clickbait" headlines and "normal" headlines, where the former are those irritating "You won't believe what X does to Y" type headlines that deliberately withhold information to try to make people click on the article. Traditionally, to write such a classifier, we would manually inspect hundreds of clickbait headlines and try to identify patterns to differentiate them from "good" headlines. We would then write a script with a lot of hand-crafted rules that tries to discriminate between clickbait headlines and good ones. This is a time consuming process that requires an expert to create the rules, and requires a lot of code maintenance, because we would probably need to continuously update and modify the rules.