This 20-hour Machine Learning with Python course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple and multiple Linear regressions; classification methods including logistic regression, discriminant analysis and naive bayes, support vector machines (SVMs) and tree based methods; cross-validation and feature selection; regularization; principal component analysis (PCA) and clustering algorithms. After successfully completing of this course, you will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions in Python.

You've spent months studying data science, now it's time to find a job in the industry. Fortunately, companies all over the world are looking to hire data scientists -- and fast. According to LinkedIn's 2020 U.S. Emerging Jobs Report, skills related to Machine Learning, Deep Learning, TensorFlow, Python, Natural Language Processing, etc. seen more than 70% annual growth. According to an IBM survey, the openings for data and analytics talent in the US will continue to increase, reaching 133% growth in 2020, and creating more than 700,000 openings. Qualified candidates will have a multitude of vacancies to choose from when ready to seek out a new position in the field.

We are so happy to announce that NYC Data Science Academy is yet again, the best data science school on SwitchUp! For the fifth year in a row, NYC Data Science Academy has topped SwitchUp's year-end list with a rating of 4.9/5 and with 265 student and alumni reviews. NYCDSA has been receiving recognition for the'Best Data Science Bootcamp' since 2016. Each year, SwitchUp ranks bootcamps from around the world based on over 15,000 verified student reviews, across over 500 bootcamps in operation, and ratings of curriculum, job support, and overall experience. This year, SwitchUp ranked the best 18 data science bootcamps around the world, based on alumni reviews.

Richard Sheng is the co-founder of QuantumViz, a big data visualization software company that allows data scientists and analysts to find insights in massive data sets, and create amazing data stories in 3D, VR, or AR. Richard worked in data science previous to taking the Deep Learning course with NYC Data Science Academy but now works as the CEO and co-founder of QuantumViz, which was his final project of the course. Before NYCDSA, Richard spent four years at TE Connectivity in Strategy and Business Development, working on strategic data science projects that have created multi-million dollar revenue impact. Prior to TE, Richard was an Investment Banker at Nomura Securities, supporting technology, media, and financial technology institutions. Before Nomura, Richard was a Principal Consultant in the SAP Data Science team, working with clients like Disney, Altria, FedEx and many other large corporations across industries.

Today on the Datanauts podcast, we talk with Vivian Zhang, a Machine Learning (ML) expert. If you've been hearing about ML from IT marketing folks and it all sounds like magic unicorn dust, this is your show. We're cutting through the cruft to get to what's real. Vivian Zhang is CTO and Chief Data Scientist at the NYC Data Science Academy. We establish a baseline of what machine learning is, how it fits into the broader category of artificial intelligence, and how ML might move the needle in IT infrastructure.

They are currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between January 11th to April 1st, 2016. This post is based on their fourth class project - Machine learning(due on the 8th week of the program). The Higgs Boson Challenge, hosted by Kaggle, asked the data scientist community to utilize machine learning to accurately predict if a particle was a Higgs-Boson particle or not; more specifically if a signal detected was either a'tau tau decay of a Higgs boson' or just'background'. The datasets provided were the training and test set with 250,000 and 550,000 observations, respectively. The training set contained all the same features as the test with two additional columns of'Label' and'Weight' that gave the accurate classifiers to help train our models.

Anthony Goldbloom is the founder and CEO of Kaggle. In 2011 & 2012, Forbes Magazine named Anthony as one of the 30 under 30 in technology, in 2013 the MIT Tech Review named him one of top 35 innovators under the age of 35 and the University of Melbourne awarded him an Alumni of Distinction Award. He holds a first call honors degree in Econometrics from the University of Melbourne. Anthony has published in the The Economist and the Harvard Business Review. Title: What Kaggle has learned from 2MM machine learning models Abstract: Kaggle is a community of over 500K data scientists who have built almost 2MM machine learning models to participate in our competitions.