Easily: Practical Machine Learning Algorithms with Python

Provides a comprehensive guide to machine learning algorithms using Python. It covers a range of supervised learning techniques like Decision Trees, Random Forests, K-Nearest Neighbors, Support Vector Machines, and Artificial Neural Networks, illustrating their application for both classification and numeric prediction. The book also introduces unsupervised learning with K-Means and dedicates chapters to assessing model performance, implementing improvement strategies, and combining algorithms for enhanced prediction. Thomas, with his background in education and psychology, emphasizes a practical, "show-what-to-do" approach for readers already familiar with Python, data science basics, and statistics.You can listen and download our episodes for free on more than 10 different platforms:https://linktr.ee/cyber_security_summaryGet the Book now from Amazon:https://www.amazon.com/Easily-Practical-Machine-Learning-Algorithms-ebook/dp/B086MNQW45?dib=eyJ2IjoiMSJ9.5OCp8sSIJ-Qz0Mw9-rU4IURJzaTNvPoFX7F06VbYy5qRlSu6dSwZ3O_ZhHAvKVMBTK8eYDUF_Q1gfJ5C0GZqq6W7nqHFesYManTYrI0z6Vq6qsjz-UdyxdhKMuH5d_OhAxIN6u1VjiAMIPBKkr9bmA.2j6a7BlJSeGpK6YoAO_DK8dIfh1T3C0sttmXZ1q9sN0&dib_tag=AUTHOR&linkCode=ll1&tag=cvthunderx-20&linkId=6d6ef6cf223dc9b8ebea22edec8b8279&language=en_US&ref_=as_li_ss_tl