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
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.
Table of Contents
Chapter 1. Unsupervised Machine Learning
Chapter 2. Deep Belief Networks
Chapter 3. Stacked Denoising Autoencoders
Chapter 4. Convolutional Neural Networks
Chapter 5. Semi-Supervised Learning
Chapter 6. Text Feature Engineering
Chapter 7. Feature Engineering Part II
Chapter 8. Ensemble Methods
Chapter 9. Additional Python Machine Learning Tools