Resources tagged with “machine-learning”
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2019)
Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd Edition
Aurélien Géron
An excellent introduction to machine learning and deep learning. Python programming knowledge required.
The Hundred-Page Machine Learning Book (2019)
Andriy Burkov
This book is an excellent introduction to machine learning and is aimed at the beginning data scientist. As such, the author doesn't shy away from mathematical concepts but provides a thorough review of the math, statistics and probability concepts that form the foundations of data science, as well as an introduction to some of the most fundamental machine learning algorithms.
Python for Data Analysis (2017)
Data Wrangling with Pandas, NumPy and IPython, 2nd Edition
Wes McKinney
This is an excellent resource for those looking to learn more about data analysis in Python. Written by the author of the open source library, pandas.
Training_Data 15 (2019)
Making Applied AI a Reality
Training_Data
Hillary Mason and other data scientists discuss some of the challenges enterprises face as they seek to develop products from basic ML prototypes/research. At the 23:30 mark, they also briefly discuss working with product managers from a data scientist's perspective.
Training_Data 19 (2019)
Implementing AI Projects
Training_Data
John Sipple, Senior Software Engineer for Machine Learning at Google and the Artificial Intelligence (AI) Portfolio Joint Reserve Lead for the Defense Innovation Unit, joins IQT CosmiQ Works’ Ryan Lewis and Nick Weir to share lessons learned from designing and executing machine learning projects. The group dives into Sipple’s past experiences, current research areas, and future AI trends and areas of interest, such as attention networks.
AI for People and Business (2019)
A Framework for Better Human Experiences and Business Success
Alex Castrounis
Presents a detailed framework to help excecutives define a roadmap for their company's AI success. Focuses on aligning AI strategy throughout the organization.