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Step-by-Step Machine Learning with Python [Video]

More Information
Learn
  • Exploit the power of Python to handle data extraction, manipulation, and exploration techniques
  • Use Python to visualize data spread across multiple dimensions and extract useful features
  • Delve into the world of analytics to predict situations correctly
  • Implement machine learning classification and regression algorithms from scratch in Python
  • Be amazed to see algorithms in action
  • Evaluate the performance of a machine learning model and optimize it
  • Solve interesting, real-world problems using machine learning and Python as the journey unfolds
About

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.

Style and Approach

This course is an enticing journey that starts from the very basics and gradually picks up pace as the story unfolds. Each concept is first succinctly defined in the larger context of things, followed by a detailed explanation of their application. Every concept is explained with the help of a project that solves a real-world problem and involves hands-on work, giving you a deep insight into the world of machine learning. With this simple yet rich language—Python—you will understand and be able to implement the examples with ease.

Features
  • Learn the fundamentals of machine learning and build your own intelligent applications
  • Master the art of building your own machine learning systems with this example-based practical guide
  • Work with important classification and regression algorithms and other machine learning techniques
Course Length 4 hours 56 minutes
ISBN9781788622370
Date Of Publication 28 Sep 2017
Exploring Naïve Bayes
The Mechanics of Naïve Bayes
The Naïve Bayes Implementation
Classifier Performance Evaluation
Model Tuning and cross-validation

Authors

Yuxi (Hayden) Liu

Yuxi (Hayden) Liu is an author of a series of machine learning books and an education enthusiast. His first book, the first edition of Python Machine Learning By Example, was a #1 bestseller in Amazon India in 2017 and 2018. His other books include R Deep Learning Projects and Hands-On Deep Learning Architectures with Python published by Packt.

He is an experienced data scientist who's focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his machine learning expertise to computational advertising, recommendation, and network anomaly detection. He published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto.