If you need a go-to guide with numerous examples and coded programs, Python Machine Learning will be the answer. If you want to train your own models and enhance them to achieve your demand, Python Machine Learning is the exact tool. Do you want to train the machines and model to carry out complex jobs with accurate date and predictions in a more efficient way? Do you want to have a quick start in the right direction to enter into the Python world? 

Machine learning is one of the most demanded fields in existence and this book aims to provide you with a reference into the more advanced world of Python and machine learning. The book will provide you with various examples and algorithms to learn and experiment with. With carefully selected topics, this book aims to serve as an expert’s guide into the world of machine learning.

Whether you are looking to apply machine learning in financial institutions to check for fraudulent transactions, or use the same to train intelligent models to detect authentic currencies, machine learning is what you seek and this book aims to provide you with a quick start in the right direction.

The central idea of this book is to cover relevant examples from real world and provide aspiring learners with a chance to experiment in a controlled environment, where even the worst of errors should pose you no issues. All you need is a good understanding of the Python language, a decent machine and a will to learn. 

This book will allow you to see how we can use data to create plots and graphs. We will mostly be visiting programs using a few datasets and comparing those using various methods. This would allow every programmer to learn the differences and gain valuable understanding of the accuracy of applied methods and functions. 

• Getting started with machine learning
• What is machine learning?
• Installing machine libraries in your system
• Supervised machine learning for discrete class label
• Machine learning methods
• K-nearest neighbors
• Decision tree
• Support vector machine
• Naive Bayes classification
• Logistic regression
• Neural network
• Supervised machine learning for continuous class label
• Regression models
• Unsupervised machine learning
• Understanding and challenges
• Dimension reduction
• Clustering models
• Working with text data
• Representing text data as bags of words
• Stopwords
• Machine learning real world applications

Even if you’ve got limited knowledge on numerous examples and coded programs currently, this book gives a proper chance to enrich your horizon on Python machine learning. 

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