Data science and machine
learning are some of the top buzzwords in the technical world today. The
resurging interest in machine learning is due to the same factors that
have made data mining and Bayesian analysis more popular than ever. This
video is your entry point to machine learning. It starts with an
introduction to machine learning and the Python language and shows you
how to complete the necessary setup. Moving ahead, you will learn all
the important concepts such as exploratory data analysis, data
preprocessing, feature extraction, data visualization and clustering,
classification, regression, and model performance evaluation. With the
help of the various projects included, you will acquire the mechanics of
several important machine learning algorithms, which will no longer
seem obscure. Also, you will be guided step-by-step to build your own
models from scratch. Toward the end, you will gather a broad picture of
the machine learning ecosystem and master best practices for applying
machine learning techniques. Throughout this course, you will learn to
tackle data-driven problems and implement your solutions with the
powerful yet simple Python language. Interesting and easy-to-follow
examples—including news topic classification, spam email detection,
online ad click-through prediction, and stock prices forecasts—will keep
you glued to the screen till you reach your goal.
About the Author
Yuxi (Hayden) Liu is
currently a data scientist working on messaging app optimization at a
multinational online media corporation in Toronto. He focuses on social
graph mining, user demographics, interest prediction, spam detection,
and recommendation systems. He has worked for several years as a data
scientist in real-time bidding programmatic advertising, where he
applied his machine learning expertise in ad optimization, click-through
rate and conversion prediction, and click fraud detection. Hayden
earned his degree from the University of Toronto, and published five
IEEE transactions and conference papers during his master's research. He
is also a machine learning education enthusiast, and has authored the
Python Machine Learning By Example book.
