Step-by-Step Machine Learning with Python هذه الدورة تدرس باللغة الإنجليزية

تفاصيل الدورة

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 examplesincluding news topic classification, spam email detection, online ad click-through prediction, and stock prices forecastswill 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.