Machine Learning: A Quantitative Approachmachine Learning: A Quantitative Approach

Python Data Science A-Z, Data Science with Machine Learning A-Z, Deep Learning A-Z, Pandas, Numpy and R Statistics

Do you find yourself unsure of how to apply your existing knowledge to Python?

Use Scikit, learn NumPy, Pandas, Matplotlib, Seaborn and dive into machine learning A-Z with Python and Data Science.

This book provides a novel framework for understanding and revising labor markets and education policies in an era of machine learning. It posits that while learning and knowing both require thinking, learning is fundamentally different than knowing because it results in cognitive processes that change over time. Learning, in contrast to knowing, requires time and agency. Therefore, “learning algorithms”—that enable machines to modify their actions based on real-world experiences—are a fundamentally new form of artificial intelligence that have potential to be even more disruptive to labor markets than prior introductions of digital technology.

This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0.