CORRELATION


Naive Principal Component Analysis in R

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Principal Component Analysis (PCA) is a technique used to find the core components that underlie different variables. Identify number of components (aka factors) In this stage, principal components (formally called'factors' at this stage) are identified among the set of variables. Cumulative var: variance added consecutively up to the last component. Cumulative proportion: the actually explained variance added consecutively up to the last component.


IT pros get a handle on machine learning and big data

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Even as an IT generalist, it pays to at least get comfortable with the matrix of machine learning outcomes, expressed with quadrants for the counts of true positives, true negatives, false positives (items falsely identified as positive) and false negatives (positives that were missed). For example, overall accuracy is usually defined as the number of instances that were truly labeled (true positives plus true negatives) divided by the total instances. If you want to know how many of the actual positive instances you are identifying, sensitivity (or recall) is the number of true positives found divided by the total number of actual positives (true positives plus false negatives). And often precision is important too, which is the number of true positives divided by all items labeled positive (true positives plus false positives).


Deep learning vs. machine learning: The difference starts with data

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The answer to the question of what makes deep learning different from traditional machine learning may have a lot... You forgot to provide an Email Address. For example, he pointed out that conventional machine learning algorithms often plateau on analytics performance after processing a certain amount of data. Comcast is also applying computer vision, audio analysis and closed-caption text analysis to video content to break movies and TV shows into "chapters" and automatically generate natural-language summaries for each chapter. Essa said that forward-thinking enterprises will find ways to leverage deep learning to develop new business models, while traditional machine learning is essentially relegated to helping businesses perform existing operations more efficiently.


Natural Language Processing @BigDataExpo #BigData #Analytics #DataScience

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"Apophenia is the propensity to see patterns in random data." We encounter it all the time in the real world. Examples include gamblers who see patterns in how the cards are being dealt or investors who imagine patterns in the movement of certain stocks, or basketball fans who believe that their favorite player has the "hot hand." But apophenia has no place in the world of data science, especially when data science is trying to help us make better decisions about critical things such as the quality of healthcare, where to allocate police resources, ensuring that our airplanes operate effectively or making investment decisions that determine our retirement readiness. Understanding the differences between epiphany (a sudden, intuitive perception of or insight into the reality) and apophenia (the perception of or belief in connectedness among unrelated phenomena) is critical as data scientists build analytic models to quantify cause and effect.


Data visualisation & machine learning courses among most valued today - Times of India

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BENGALURU: The humongous amount of digital data being generated, and companies' need to glean insights and make predictions from them have made skills in data visualisation, data science, and machine learning among the most valued for technology recruiters today. This is reflected in the number of working professionals signing up for specialised courses in these spaces. Candidates who complete the courses tend to get between 20% and 50% increase in salaries. Kashyap Dalal, chief business officer at online learning platform Simplilearn, says that big data and analytics courses were the big growth drivers in the past three years. While data science continues to remain popular, accounting for 30% of all learners, courses on visualisation tools and machine learning have become very attractive over the past six months, he said.


Machine Learning in R for beginners

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Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical machine learning tasks are concept learning, function learning or "predictive modeling", clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions, for example. Machine learning hopes that including the experience into its tasks will eventually improve the learning. The ultimate goal is to improve the learning in such a way that it becomes automatic, so that humans like ourselves don't need to interfere any more.


3 Practical Ways Artificial Intelligence Can Enhance Marketing Creativity Right Now

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Eighty percent of marketing leaders say that AI will "revolutionize" marketing by 2020, but many CMOs remain paralyzed on the sidelines, questioning how this kind of bleeding-edge tech should be used and if it will provide a marked return on investment. Ironically enough, at the same time we talk about the uncertainty that an artificial intelligence-powered future could bring, we are increasingly adopting these experiences into our day-to-day lives. From Siri and self-driving cars to connected devices like Amazon Echo and customer service chatbots, experiences powered by artificial intelligence will soon be the norm. People might not fully comprehend the growing role of AI in their lives, but they're adopting it regardless, sometimes even unbeknownst to them. Even though marketers generally accept AI's growing influence, they hesitate to adopt it (though they may not realize that if their brand invests in programmatic media buying, for example, they're already deeply immersed in the world of AI).


Feature Engineering using R

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Feature Engineering is paramount in building a good predictive model. It's significant to obtain a deep understanding of the data that is being used for analysis. The characteristics of the selected features are definitive of a good training model. This blog aims to go through a few well-known methods for selecting, filtering or constructing new features in a quick R way as opposed to helping you understand the theoretical mathematics or statistics behind it. Note that this is by no means an exhaustive list of methods and I try to keep the concepts crisp and to the point.


Biologically Inspired Software Architecture for Deep Learning – Intuition Machine

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With the emergence of Deep Learning as the dominant paradigm for Artificial Intelligence based systems, one open question that seems to be neglected is "What guidelines do we have in architecting software that uses Deep Learning?" If all the innovative companies like Google are on a exponential adoption curve to incorporate Deep Learning in every thing they do, then what perhaps is the software architecture that holds this all together? The folks at Google wrote a paper (a long time ago, meaning 2014), "Machine Learning: The High-Interest Credit Card of Technical Debt" that enumerates many of the difficulties that we need to consider when building software that consists of machine learning or deep learning sub-components. Contrary to popular perception that that Deep Learning systems can be "self-driving". There is a massive ongoing maintenance cost when machine learning is used.


Feature Engineering using R

#artificialintelligence

Feature Engineering is paramount in building a good predictive model. It's significant to obtain a deep understanding of the data that is being used for analysis. The characteristics of the selected features are definitive of a good training model. This blog aims to go through a few well-known methods for selecting, filtering or constructing new features in a quick R way as opposed to helping you understand the theoretical mathematics or statistics behind it. Note that this is by no means an exhaustive list of methods and I try to keep the concepts crisp and to the point.