Online Data Science Course : Data Science Certification Course

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Data Science has become the new desirable IT job. While there are only few in the market conversant with the terms like python, machine learning, deep learning and transflow, it is also a fact that these skills are high in demand. Acadgild will transform you into a Data Scientist by delivering hands-on experience in Statistics, Machine Learning, Deep Learning and Artificial Intelligence (AI) using Python, TensorFlow, Apache Spark, R and Tableau. The course provides in-depth understanding of Machine Learning and Deep Learning algorithms such as Linear Regression, Logistic Regression, Naive Bayes Classifiers, Decision Tree and Random Forest, Support Vector Machine, Artificial Neural Networks and more. This 24 weeks long Data Science course has several advantages like 400 total coding hours and experienced industry mentors.


Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions

arXiv.org Machine Learning

Dynamic Boltzmann Machine (DyBM) has been shown highly efficient to predict time-series data. Gaussian DyBM is a DyBM that assumes the predicted data is generated by a Gaussian distribution whose first-order moment (mean) dynamically changes over time but its second-order moment (variance) is fixed. However, in many financial applications, the assumption is quite limiting in two aspects. First, even when the data follows a Gaussian distribution, its variance may change over time. Such variance is also related to important temporal economic indicators such as the market volatility. Second, financial time-series data often requires learning datasets generated by the generalized Gaussian distribution with an additional shape parameter that is important to approximate heavy-tailed distributions. Addressing those aspects, we show how to extend DyBM that results in significant performance improvement in predicting financial time-series data.


The Perceptron

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Most tasks in Machine Learning can be reduced to classification tasks. For example, we have a medical dataset and we want to classify who has diabetes (positive class) and who doesn't (negative class). We have a dataset from the financial world and want to know which customers will default on their credit (positive class) and which customers will not (negative class). To do this, we can train a Classifier with a'training dataset' and after such a Classifier is trained (we have determined its model parameters) and can accurately classify the training set, we can use it to classify new data (test set). If the training is done properly, the Classifier should predict the class probabilities of the new data with a similar accuracy.


PyData Carolinas 2016 Presentation: Deep Finch? A Continued Comparison of Machine Learning Models to Label Birdsong Syllables

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Songbirds provide a model system that neuroscientists use to understand how the brain learns and controls speech and similar skills. Much like infants learning to speak from their parents, songbirds learn their song from a tutor and practice it millions of times before reaching maturity. Also like humans, songbirds have evolved special brain regions for learning and producing their vocalizations. These newly-evolved brain regions in songbirds, known as the song system, are found within broader brain areas shared by birds and humans across evolution. So by studying how the song system works, we can learn about our own brains.


Which is your favorite Machine Learning Algorithm?

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Developed back in the 50s by Rosenblatt and colleagues, this extremely simple algorithm can be viewed as the foundation for some of the most successful classifiers today, including suport vector machines and logistic regression, solved using stochastic gradient descent. The convergence proof for the Perceptron algorithm is one of the most elegant pieces of math I've seen in ML. Most useful: Boosting, especially boosted decision trees. This intuitive approach allows you to build highly accurate ML models, by combining many simple ones. Boosting is one of the most practical methods in ML, it's widely used in industry, can handle a wide variety of data types, and can be implemented at scale.