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Advanced Machine Learning with Python, by John Hearty
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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
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 approachThis 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.
- Sales Rank: #359492 in Books
- Published on: 2016-07-28
- Released on: 2016-07-28
- Original language: English
- Dimensions: 9.25" h x .63" w x 7.50" l, 1.06 pounds
- Binding: Paperback
- 278 pages
About the Author
John Hearty
John Hearty is a consultant in digital industries with substantial expertise in data science and infrastructure engineering. Having started out in mobile gaming, he was drawn to the challenge of AAA console analytics. Keen to start putting advanced machine learning techniques into practice, he signed on with Microsoft to develop player modelling capabilities and big data infrastructure at an Xbox studio. His team made significant strides in engineering and data science that were replicated across Microsoft Studios. Some of the more rewarding initiatives he led included player skill modelling in asymmetrical games, and the creation of player segmentation models for individualized game experiences. Eventually John struck out on his own as a consultant offering comprehensive infrastructure and analytics solutions for international client teams seeking new insights or data-driven capabilities. His favourite current engagement involves creating predictive models and quantifying the importance of user connections for a popular social network. After years spent working with data, John is largely unable to stop asking questions. In his own time, he routinely builds ML solutions in Python to fulfil a broad set of personal interests. These include a novel variant on the StyleNet computational creativity algorithm and solutions for algo-trading and geolocation-based recommendation. He currently lives in the UK.
Most helpful customer reviews
31 of 31 people found the following review helpful.
When you want to progress
By braxen
A few word about myself:
I am a Analyst, I have a MSc. in Mathematics and Statistics and do analytics for a living. While I have studied about neural networks and machine learning a while ago, only past year have I (re)-discovered the power of neural nets and Deep Learning.
In my quest to improve my knowledge, I have taken many certificates in ML and have bought a few books about Machine Learning. Among these are:
-Python Machine Learning by Sebastian Raschka (recommended)
-Building Machine Learning Systems with Python by by Luis Pedro Coelho and Willi Richert (nice to have for additional perspective)
However, I wanted to go beyond what one can find in those two books. The topics I was specifically interested in were:
-Deep Belief Networks (inc. Restricted Boltzmann Machine)
-Autoencoders
-Convolutional Neural Networks
So where does Advanced Machine Learning rank among these?
I must say, and that will be my main criticism of the book that it is not for the faint of heart. It is fast, sometimes too fast... I suppose there is so much you can put in 250 pages to explain about these topics, and it is easy to become lost.
However, do not get me wrong. This book is a small gem in itself.
Why? Because while I have found online many tutorials or courses about the topics I was interested, the book gives you additional information and explanations that I haven't found anywhere else. How do you set your hyper-parameters in a CNN? What is the depth exactly representing, what are the current architectures, are they really all that good? Why?
It is the difference between the how and the more precise what and why. Tutorials online are great but many people just do things without clearly showing why. This books gives you the clues.
In conclusion, for me currently (after having bought 8 books):
The book is difficult but not super difficult. It gives more understanding and depth than I could ever obtain with all the material available online currently (including the very good Stanford courses). So, yes, I feel I am making progress.
-Python Machine Learning by Sebastian Raschka is the way to go for Machine Learning foundations
-Advanced Machine Learning with Python by John Hearty is a super helpful complement to what one can already find online dispersed all over the place, it just make sense with better hindsight.
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