Advanced Machine Learning with Python: Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python
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Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python Key Features 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 Book Description 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. 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 Who this book is for: ๏ปฟThis 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. Review: Useful book - It is very good book and cover the information that we looking for Review: Helpful for learning - Worth it
| Best Sellers Rank | 2,725,611 in Books ( See Top 100 in Books ) 19,085 in Computing & Internet Programming |
| Customer Reviews | 3.9 out of 5 stars 15 Reviews |
N**I
Useful book
It is very good book and cover the information that we looking for
A**T
Helpful for learning
Worth it
A**R
Five Stars
Great product!
J**S
poor print quality, complete in black and white,thus figures unreadable, errors in example code
I bought this book, because I liked the idea of explaining concepts as they are implemented in python. Unfortunately, the quality of this book is very poor. It is entirely printed in b/w. That makes figures unreadable. Example: A color coded heat map just printed in b/w. In particular if you also add number into the mix, so that everything is just a dark grey blob. Or a plot of clustered data point in which the different classes can be distinguished only by a tiny change in their grey shading, maybe. The text itself looks just cheaply printed. Again, printed in b/w, which makes the python-listings hard to read and understand. This makes understanding whats going on harder than it need to be. For the price of around 37โฌ, the code could have been printed in a better readable quality with some form of syntax highlighting. The code itself contains some errors. That is strange, since it should have been executed by the author,no?
B**N
When you want to progress
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.
P**R
Full of printing errors
This book has so many printing errors in its code that it is impossible to follow. They claim to teach ML using python but least care has been taken to verify the printed contents.
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