Python: Deeper Insights into Machine Learning

Solve challenging data science problems by mastering cutting-edge machine

learning techniques in Python About This Book - Resolve complex machine

learning problems and explore deep learning - Learn to use Python code for

implementing a range of machine learning algorithms and techniques - A

practical tutorial that tackles real-world computing problems through a

rigorous and effective approach Who This Book Is For This title is for Python

developers and analysts or data scientists who are looking to add to their

existing skills by accessing some of the most powerful recent trends in data

science. If you've ever considered building your own image or text-tagging

solution, or of entering a Kaggle contest for instance, this book is for you!

Prior experience of Python and grounding in some of the core concepts of

machine learning would be helpful. What You Will Learn - Compete with top data

scientists by gaining a practical and theoretical understanding of cutting-

edge deep learning algorithms - Apply your new found skills to solve real

problems, through clearly-explained code for every technique and test -

Automate large sets of complex data and overcome time-consuming practical

challenges - Improve the accuracy of models and your existing input data using

powerful feature engineering techniques - Use multiple learning techniques

together to improve the consistency of results - Understand the hidden

structure of datasets using a range of unsupervised techniques - Gain insight

into how the experts solve challenging data problems with an effective,

iterative, and validation-focused approach - Improve the effectiveness of your

deep learning models further by using powerful ensembling techniques to strap

multiple models together In Detail Designed to take you on a guided tour of

the most relevant and powerful machine learning techniques in use today by top

data scientists, this book is just what you need to push your Python

algorithms to maximum potential. Clear examples and detailed code samples

demonstrate deep learning techniques, semi-supervised learning, and more - all

whilst working with real-world applications that include image, music, text,

and financial data. The machine learning techniques covered in this book are

at the forefront of commercial practice. They are applicable now for the first

time in contexts such as image recognition, NLP and web search, computational

creativity, and commercial/financial data modeling. Deep Learning algorithms

and ensembles of models are in use by data scientists at top tech and digital

companies, but the skills needed to apply them successfully, while in high

demand, are still scarce. This book is designed to take the reader on a guided

tour of the most relevant and powerful machine learning techniques. Clear

descriptions of how techniques work and detailed code examples demonstrate

deep learning techniques, semi-supervised learning and more, in real world

applications. We will also learn about NumPy and Theano. By this end of this

book, you will learn a set of advanced Machine Learning techniques and acquire

a broad set of powerful skills in the area of feature selection & feature

engineering. Style and approach This book focuses on clarifying the theory and

code behind complex algorithms to make them practical, useable, and well-

understood. Each topic is described with real-world applications, providing

both broad contextual coverage and detailed guidance.