There are several machine learning skills that are in high demand in the global marketplace today. The skill most required is the ability to come up with fundamental innovations in machine learning, and implement them to solve practical problems. For a research career in AI, you need a PhD, preferably from a well-known programme, and research competence as demonstrated by published papers, implemented solutions and peer acceptance. For those at the forefront of research, the sky is the limit, and seven-figure USD salaries are not infrequent. The next tier of demand is for people who can build practical implementations, especially in collaboration with a cutting-edge research team.

There are several machine learning skills that are in high demand in the global marketplace today. The skill most required is the ability to come up with fundamental innovations in machine learning, and implement them to solve practical problems. For a research career in AI, you need a PhD, preferably from a well-known programme, and research competence as demonstrated by published papers, implemented solutions and peer acceptance. For those at the forefront of research, the sky is the limit, and seven-figure USD salaries are not infrequent. The next tier of demand is for people who can build practical implementations, especially in collaboration with a cutting-edge research team.

We are living in an era driven by algorithms and more specifically deep learning algorithms which are beginning to pervading and potentially intruding every single facet of our personal and professional lives. When algorithms begin playing a commanding role in our everyday personal choices including clothes, shoes, movies, music, content, jobs, whatever and start dictating what is best for us and what is not, we have to concede that we are already in the midst of algorithms driven enlightenment, based on whichever camp we want to be in. Consider their application in myriad esoteric use cases - sorting and grading cucumbers; creating movie trailers; writing news articles; measuring productivity of cows; predicting students likely to drop out; optimizing soil nutrient levels and many more such use cases. To a battle for supremacy on the'senses' dimension against the human race including vision, speech and text, these algorithms are proving their mettle in every walk of human life. So it is really high time that we bow to the powers of these very powerful algorithms and be led by them in this algorithms-driven insights economy.

The release of two machine learning (ML) model builders have made it easier for software engineers to create and run ML models, even without specialized training. Microsoft and Amazon Web Services' (AWS) Gluon is an open source project that eliminates some of the difficult work required to develop artificial intelligence (AI) systems. It provides training algorithms and neural network models, two important components of a deep learning system, that developers can use to develop their own ML systems. Google's ML engine is part of its cloud platform and is offered as a managed service for developers to build ML models that work on any type of data, of any size. Similar to Gluon, Google's service provides pre-trained models for developers to generate their own tailored ML models.

This is the SECOND in a series of posts on applying Tim Ferriss' accelerated learning framework to Data Science. My goal is to become a world-class (top 5%) Data Scientist in 6 months, while open-sourcing everything I find and learn along the way. And if you stick around until the end, you're in for a special treat. A simple Google search of "how to learn Data Science" returns thousands of learning plans, degree programs, tutorials, and bootcamps. It's never been more difficult for a beginner to find signal in the noise. Everyone seems to have a different opinion, and the only common approach appears to be dumping a long list of courses to take and books to read, all the while providing little to no context into how these concepts fit into the bigger picture.

Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time we dive deep into Machine Learning.

Who would have thought that the stories around self-driven cars could actually come true, so much so that machine learning algorithms can enable computers to communicate with humans, drive cars, play games and do things human cannot do. Machine Learning with its mathematical algorithms and scientific innovations have become a huge part of our lives. For example, when Google auto-corrects a misspelled word, it applies probability algorithm, an action performed using Machine Learning, which compares the database of the previous searches done by millions of other users and predicts the word we intend to use. With the ever-increasing knowledge in science and technology, machine learning is not far behind to be the new switchboard for Higher Education, personalising education at all levels. It reads and identifies the data patterns to inform algorithms that can make data-driven predictions and decisions.

Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem. This approach has been successfully used in various models and large-scale applications. In this review, we give an overview of recent trends in variational inference. We first introduce standard mean field variational inference, then review recent advances focusing on the following aspects: (a) scalable VI, which includes stochastic approximations, (b) generic VI, which extends the applicability of VI to a large class of otherwise intractable models, such as non-conjugate models, (c) accurate VI, which includes variational models beyond the mean field approximation or with atypical divergences, and (d) amortized VI, which implements the inference over local latent variables with inference networks. Finally, we provide a summary of promising future research directions.

We present the Variational Adaptive Newton (VAN) method which is a black-box optimization method especially suitable for explorative-learning tasks such as active learning and reinforcement learning. Similar to Bayesian methods, VAN estimates a distribution that can be used for exploration, but requires computations that are similar to continuous optimization methods. Our theoretical contribution reveals that VAN is a second-order method that unifies existing methods in distinct fields of continuous optimization, variational inference, and evolution strategies. Our experimental results show that VAN performs well on a wide-variety of learning tasks. This work presents a general-purpose explorative-learning method that has the potential to improve learning in areas such as active learning and reinforcement learning.

This paper presents a robust matrix elastic net based canonical correlation analysis (RMEN-CCA) for multiple view unsupervised learning problems, which emphasizes the combination of CCA and the robust matrix elastic net (RMEN) used as coupled feature selection. The RMEN-CCA leverages the strength of the RMEN to distill naturally meaningful features without any prior assumption and to measure effectively correlations between different 'views'. We can further employ directly the kernel trick to extend the RMEN-CCA to the kernel scenario with theoretical guarantees, which takes advantage of the kernel trick for highly complicated nonlinear feature learning. Rather than simply incorporating existing regularization minimization terms into CCA, this paper provides a new learning paradigm for CCA and is the first to derive a coupled feature selection based CCA algorithm that guarantees convergence. More significantly, for CCA, the newly-derived RMEN-CCA bridges the gap between measurement of relevance and coupled feature selection. Moreover, it is nontrivial to tackle directly the RMEN-CCA by previous optimization approaches derived from its sophisticated model architecture. Therefore, this paper further offers a bridge between a new optimization problem and an existing efficient iterative approach. As a consequence, the RMEN-CCA can overcome the limitation of CCA and address large-scale and streaming data problems. Experimental results on four popular competing datasets illustrate that the RMEN-CCA performs more effectively and efficiently than do state-of-the-art approaches.