NYC Data Science Academy

This is a 6-week evening program providing a hands-on introduction to the Hadoop and Spark ecosystem of Big Data technologies. The course will cover these key components of Apache Hadoop: HDFS, MapReduce with streaming, Hive, and Spark. Programming will be done in Python. The course will begin with a review of Python concepts needed for our examples. The course format is interactive. Students will need to bring laptops to class.

Learn about the concept of the likelihood function as it supervises models from logistic regression to deep learning. About this Event Machine learning enables a system to learn from data instead of explicit programming. It is a form of AI that can produce predictions as a predictive algorithm trains input data. As data science professionals, we must ensure that our models will provide accurate predictions at best, so we develop ways to monitor our predictive model's performance at every stage. Join us on October 28, Wednesday, and learn about the concept of the likelihood function in logistic regression. A likelihood function is a widely accepted concept for such measurement, from a simple linear model such as logistic regression to a sophisticated neural network. Moreover, you will understand that a model can be taken for a family of parameterized distributions. This idea will also be generalized to a simple linear regression to introduce the likelihood approach to train a model. This workshop is best for professionals interested in learning more about the practical applications of AI and machine learning. Save your spot herehttps://info.nycdatascience.com/how-strong-is-your-machine-learning-model?utm_source=meetup&utm_medium=referral&utm_campaign=how-strong-is-your-machine-learning-model

This course is a 35-hour program designed to provide a comprehensive introduction to R. You’ll learn how to load, save, and transform data as well as how to write functions, generate graphs, and fit basic statistical models with data. In addition to a theoretical framework in which you will learn the process of data analysis, this course focuses on the practical tools needed in data analysis and visualization. By the end of the course, you will have mastered the essential skills of processing, manipulating and analyzing data of various types, creating advanced visualizations, generating reports, and documenting your codes.

This 35-hour Machine Learning with R course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications in R. It will introduce you to data mining, performance measures and dimension reduction, regression models, both linear and generalized, KNN and Naïve Bayes models, tree models, and SVMs as well as the Association Rule for analysis. After successfully completing of this course, you will be able to break down the mathematics behind major machine learning algorithms, explain the principles of machine learning algorithms, and implement these methods to solve real-world problems.