For more information contact us      Phone: +1-425-748-5018   Fax: +1-425-644-2185   TrainingUSA@selagroup.com Coming courses Register/More info More courses from Big Data  Deep Learning for Software Developers (of Machine Learning) Machine Learning Fundamentals Elastic Stack Workshop Using solr search engine Big Data for Decision Makers Machine Learning for Software Developers Big Data with Hadoop and Spark Testing the Internet of Things (IoT) – An Exploratory Workshop

MLSD - Version: 1 Machine Learning for Software Developers 2 days course Description Machine learning expands the boundaries of what's possible by letting software accomplish tasks that can't be accomplished algorithmically. In this hands-on workshop, learn the fundamentals of machine learning from a software developer's perspective and learn how to use it to build and operationalize sophisticated predictive models. You’ll learn about clustering, regression, classification, Support Vector Machines, and more, and you’ll do it with Scikit-learn, the world’s most popular machine-learning library. You'll also see examples of machine learning in action, train models of your own to perform tasks such as facial recognition, and go away with lots of sample code to kick-start your next project. Intended audience Software Developers Objectives Expanding the boundaries of what's possible by letting software accomplish tasks that can't be accomplished algorithmically. Topics Introduction to Machine Learning Learn what machine learning is, what types of machine-learning models there are, and how to use Scikit-learn to build simple unsupervised- and supervised-learning models using algorithms such as k-means clustering and k-nearest neighbors. Also learn the basics of data cleaning and preparation. Regression Learn how to build supervised-learning models that use regression algorithms to predict numeric values such as how long a flight will be delayed or how much a house might sell for. Also learn how to score regression models for accuracy, how to handle categorical values in datasets, and about popular regression algorithms such as linear regression, random forests, and gradient-boosting machines (GBMs). Binary Classification Learn how to build classification models that predict binary outcomes such as whether a flight will or will not arrive on time. Also learn about precision, recall, confusion matrices, and other metrics used to score binary-classification models, and how to build machine-learning models around textual data – for example, models that predict whether an e-mail is “spam” or “not spam” and models that analyze text for sentiment. Multiclass Classification Learn how to build classification models that predict non-binary outcomes such as what character a hand-written digit represents or what category a document belongs to. Also take a deep dive into Support Vector Machines (SVMs), hyperparameter tuning, and data normalization, and put your skills to work building a facial-recognition model.

MLSD Course
	I would like a representative to contact me regarding this activity
	*	*
	* *	*