What do you think?
Rate this book
Mastering Machine Learning Algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition
Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems
Key FeaturesUpdated to include new algorithms and techniquesCode updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applicationsBook DescriptionMastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains.
You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks.
By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
What you will learnUnderstand the characteristics of a machine learning algorithmImplement algorithms from supervised, semi-supervised, unsupervised, and RL domainsLearn how regression works in time-series analysis and risk predictionCreate, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANsWho this book is forThis book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.
Table of ContentsMachine Learning Model FundamentalsLoss functions and RegularizationIntroduction to Semi-Supervised LearningAdvanced Semi-Supervised ClassifiationGraph-based Semi-Supervised LearningClustering and Unsupervised ModelsAdvanced Clustering and Unsupervised ModelsClustering and Unsupervised Models for MarketingGeneralized Linear Models and RegressionIntroduction to Time-Series AnalysisBayesian Networks and Hidden Markov ModelsThe EM AlgorithmComponent Analysis and Dimensionality ReductionHebbian LearningFundamentals of Ensemble LearningAdvanced Boosting AlgorithmsModeling Neural NetworksOptimizing Neural NetworksDeep Convolutional NetworksRecurrent Neural NetworksAuto-EncodersIntroduction to Generative Adversarial Networks</
Key FeaturesUpdated to include new algorithms and techniquesCode updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applicationsBook DescriptionMastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains.
You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks.
By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
What you will learnUnderstand the characteristics of a machine learning algorithmImplement algorithms from supervised, semi-supervised, unsupervised, and RL domainsLearn how regression works in time-series analysis and risk predictionCreate, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANsWho this book is forThis book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required.
Table of ContentsMachine Learning Model FundamentalsLoss functions and RegularizationIntroduction to Semi-Supervised LearningAdvanced Semi-Supervised ClassifiationGraph-based Semi-Supervised LearningClustering and Unsupervised ModelsAdvanced Clustering and Unsupervised ModelsClustering and Unsupervised Models for MarketingGeneralized Linear Models and RegressionIntroduction to Time-Series AnalysisBayesian Networks and Hidden Markov ModelsThe EM AlgorithmComponent Analysis and Dimensionality ReductionHebbian LearningFundamentals of Ensemble LearningAdvanced Boosting AlgorithmsModeling Neural NetworksOptimizing Neural NetworksDeep Convolutional NetworksRecurrent Neural NetworksAuto-EncodersIntroduction to Generative Adversarial Networks</
1217 pages, Kindle Edition
Published January 31, 2020
15 people are currently reading
25 people want to read
About the author
Giuseppe Bonaccorso
18 books3 followersExperienced and goal-oriented senior executive leader with wide expertise in the management of Artificial Intelligence, Machine Learning, Deep Learning, and Data Science projects for healthcare, B2C and Military industries (Fortune 500 firms).
His main interests include Machine/Deep Learning, Reinforcement Learning, Advanced Analytics, Bio-inspired adaptive systems, Business Intelligence, Neuroscience, Neural Language Processing, Econometrics, Data Science Strategy and Organization.
Professional member of IEEE, IEEE Computer Society, AAAI, ACM, IAENG, AICA, SFIA, and Agile Manifesto.
His main interests include Machine/Deep Learning, Reinforcement Learning, Advanced Analytics, Bio-inspired adaptive systems, Business Intelligence, Neuroscience, Neural Language Processing, Econometrics, Data Science Strategy and Organization.
Professional member of IEEE, IEEE Computer Society, AAAI, ACM, IAENG, AICA, SFIA, and Agile Manifesto.
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 2 of 2 reviews
April 27, 2020
Every serious machine learning engineer/practitioner needs this book. Full stop.
There is something here for everyone. All of the classic/early ML algorithms/techniques are fully explained, including optimizers, loss functions, etc. The more advanced topics are masterfully handled as well.
Not only do we get excellent mathematical exposition of the above, but the accompanying code and examples are clear, practical, and relevant.
Now, in good conscience, I could not recommend this book to a complete beginner to ML. I believe there are more accessible texts out there for someone just looking to implement the libraries without getting deep into the whys and hows. So it is more of an intermediate to advanced text, but it doesn't mean that it reads like an academic paper either. Those with some mathematical training will enjoy it more, but very accessible nonetheless. There are little errors here and there but nothing so egregious as to discredit the entire content.
All in all, this is an excellent text for reference, an advanced course in ML, or just to have on hand to show you mean business. It should cost a lot more!
There is something here for everyone. All of the classic/early ML algorithms/techniques are fully explained, including optimizers, loss functions, etc. The more advanced topics are masterfully handled as well.
Not only do we get excellent mathematical exposition of the above, but the accompanying code and examples are clear, practical, and relevant.
Now, in good conscience, I could not recommend this book to a complete beginner to ML. I believe there are more accessible texts out there for someone just looking to implement the libraries without getting deep into the whys and hows. So it is more of an intermediate to advanced text, but it doesn't mean that it reads like an academic paper either. Those with some mathematical training will enjoy it more, but very accessible nonetheless. There are little errors here and there but nothing so egregious as to discredit the entire content.
All in all, this is an excellent text for reference, an advanced course in ML, or just to have on hand to show you mean business. It should cost a lot more!
September 13, 2020
Don't fall prey to the hype and to the incredible recommendations this book claims. I’ve read it from cover to cover and now I can tell you the truth about it.
This book looks like being just a bad copy of some university textbook on machine learning.
It is stuffed with mathematical formulations that the author absolutely doesn't bother to explain to the reader. In countless parts of the book, the author presents one formula after the other and candidly closes with “as you can easily understand from the previous formula” LOL the author cannot even imagine readers are buying the book because they expected someone to explain the formulas to them.
Finally, the Python code is badly written, unscalable beyond the toy datasets the author used for the book and it won't really work with any real world data science problem.
If you need a book on the theory of machine learning get something serious. If you need a theoretical book on machine learning don't get deceived, but instead buy Machine Learning: A Probabilistic Perspective by Kevin Murphy or Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David.
This book looks like being just a bad copy of some university textbook on machine learning.
It is stuffed with mathematical formulations that the author absolutely doesn't bother to explain to the reader. In countless parts of the book, the author presents one formula after the other and candidly closes with “as you can easily understand from the previous formula” LOL the author cannot even imagine readers are buying the book because they expected someone to explain the formulas to them.
Finally, the Python code is badly written, unscalable beyond the toy datasets the author used for the book and it won't really work with any real world data science problem.
If you need a book on the theory of machine learning get something serious. If you need a theoretical book on machine learning don't get deceived, but instead buy Machine Learning: A Probabilistic Perspective by Kevin Murphy or Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David.
Displaying 1 - 2 of 2 reviews