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This is the code repository for Python Machine Learning By Example, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
This book starts with an introduction to machine learning and the Python language and shows you how to complete the 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 various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. 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 best practices of applying machine learning techniques.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
Chapter 1 is introductory and does not contain any code. All other chapters contain code. Some data files can be found in the folders, others can be downloaded from the links provided in the chapters.
The code will look like the following:
>>> from nltk.corpus import names
>>> from nltk.stem import WordNetLemmatizer
>>> def letters_only(astr):
The following are required for you to utilize this book: scikit-learn 0.18.0 Numpy 1.1 Matplotlib 1.5.1 NLTK 3.2.2 pandas 0.19.2 GraphViz Quandl Python API You can use a 64-bit architecture, 2GHz CPU, and 8GB RAM to perform all the steps in this book. You will require at least 8GB of hard disk space..
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