In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Lastly, we also have a responsibility to make sure that new waves of technology don't leave anyone behind. That means investing in modern skills training to ensure the global workforce has the knowledge and experience to work in partnership with technologies like AI or blockchain, and is prepared for the "new collar" jobs this era will create. That means building a new paradigm for education that includes strong linkage between jobs and school, a renewal of focus on apprenticeships, and lifelong learning beyond the diploma.

Lastly, we also have a responsibility to make sure that new waves of technology don't leave anyone behind. That means investing in modern skills training to ensure the global workforce has the knowledge and experience to work in partnership with technologies like AI or blockchain, and is prepared for the "new collar" jobs this era will create. That means building a new paradigm for education that includes strong linkage between jobs and school, a renewal of focus on apprenticeships, and lifelong learning beyond the diploma.

About this course: This is the second course in the Data Warehousing for Business Intelligence specialization. Ideally, the courses should be taken in sequence. In this course, you will learn exciting concepts and skills for designing data warehouses and creating data integration workflows. These are fundamental skills for data warehouse developers and administrators. You will have hands-on experience for data warehouse design and use open source products for manipulating pivot tables and creating data integration workflows.You will also gain conceptual background about maturity models, architectures, multidimensional models, and management practices, providing an organizational perspective about data warehouse development.

Advanced Data Mining Projects with R takes you one step ahead in understanding the most complex data mining algorithms and implementing them in the popular R language. Follow up to our course Data Mining Projects in R, this course will teach you how to build your own recommendation engine. You will also implement dimensionality reduction and use it to build a real-world project. Going ahead, you will be introduced to the concept of neural networks and learn how to apply them for predictions, classifications, and forecasting. Finally, you will implement ggplot2, plotly and aspects of geomapping to create your own data visualization projects.By the end of this course, you will be well-versed with all the advanced data mining techniques and how to implement them using R, in any real-world scenario.

Many developers (including myself) have included learning machine learning in their new year resolutions for 2018. Even after blocking an hour everyday in the calendar, I am hardly able to make progress. The key reason for this is the confusion on where to start and how to get started. It is overwhelming for an average developer to get started with machine learning. There are many tutorials, MOOCs, free resources, and blogs covering this topic. But they are only adding to the confusion by making it look complex.

Feature selection problems arise in a variety of applications, such as microarray analysis, clinical prediction, text categorization, image classification and face recognition, multi-label learning, and classification of internet traffic. Among the various classes of methods, forward feature selection methods based on mutual information have become very popular and are widely used in practice. However, comparative evaluations of these methods have been limited by being based on specific datasets and classifiers. In this paper, we develop a theoretical framework that allows evaluating the methods based on their theoretical properties. Our framework is grounded on the properties of the target objective function that the methods try to approximate, and on a novel categorization of features, according to their contribution to the explanation of the class; we derive upper and lower bounds for the target objective function and relate these bounds with the feature types. Then, we characterize the types of approximations taken by the methods, and analyze how these approximations cope with the good properties of the target objective function. Additionally, we develop a distributional setting designed to illustrate the various deficiencies of the methods, and provide several examples of wrong feature selections. Based on our work, we identify clearly the methods that should be avoided, and the methods that currently have the best performance.

Cluster analysis is a staple of unsupervised machine learning and data science. It is very useful for data mining and big data because it automatically finds patterns in the data, without the need for labels, unlike supervised machine learning. In a real-world environment, you can imagine that a robot or an artificial intelligence won't always have access to the optimal answer, or maybe there isn't an optimal correct answer. You'd want that robot to be able to explore the world on its own, and learn things just by looking for patterns. Do you ever wonder how we get the data that we use in our supervised machine learning algorithms?

In previous posts, I introduced Keras for building convolutional neural networks and performing word embedding. The next natural step is to talk about implementing recurrent neural networks in Keras. In a previous tutorial of mine, I gave a very comprehensive introduction to recurrent neural networks and long short term memory (LSTM) networks, implemented in TensorFlow. In this tutorial, I'll concentrate on creating LSTM networks in Keras, briefly giving a recap or overview of how LSTMs work. In this Keras LSTM tutorial, we'll implement a sequence-to-sequence text prediction model by utilizing a large text data set called the PTB corpus. All the code in this tutorial can be found on this site's Github repository. Recommended online course: If you are more of a video course learner, I'd recommend this inexpensive Udemy course to learn more about Keras and LSTM networks: Zero to Deep Learning with Python and Keras A LSTM network is a kind of recurrent neural network.

The programme aims to produce AI engineers with a highly relevant skillset in AI topics. Students will learn how to use and develop intelligent computer systems that can learn from experience, recognise patterns in vast amounts of data and reason strategically in complex decision-making situations. The programme content will deliver a comprehensive range of topics integral to the study of AI. These include machine learning, deep learning, natural language processing, optimisation, anomaly detection and big data processing to name but a few. The programme will start in September 2018 producing the first AI graduates in 2019.