What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Key benefits

Updated to include new algorithms and techniques

Code updated to Python 3.8 & TensorFlow 2.x

New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applications

Description

Mastering 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 learn

Understand the characteristics of a machine learning algorithm

Implement algorithms from supervised, semi-supervised, unsupervised, and RL domains

Learn how regression works in time-series analysis and risk prediction

Create, 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 GANs

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

$48.99

$54.99

Machine Learning for Algorithmic Trading

$57.99

Total $ 161.97

hawkinflight Aug 13, 2020

The book covers a lot of methods, as can be seen in the table of contents. This provides a nice breadth. What is really nice, is that before getting into any of the methods, the author starts with a chapter on ML Model Fundamentals. In this chapter, he presents not just the how's but also the why's of scaling data before modeling. He also talks about model capacity, bias and variance of an estimator, and how these relate to under or over fitting a model. Recently, I have heard two things during presentations: 1) "I won't go over scaling, because I think we all know when to apply these transformations" 2) "I don't even know what you're talking about" when asked about a bias/variance trade-off. I think it's great to have the opportunity to read a practitioner's explanation of these matters. He also talks about the options for splitting a data set into portions: 1) training, validation, and test portions, or 2) just into training and test portions, or 3) using a cross-validation approach. I have taken an ML class on Coursera where the first option was used, and the others weren't mentioned. It's nice to see all three here. He helps the reader by noting that some topics he's introducing will be discussed in more detail in the next few chapters. That's nice because questions start arising in your head, and then you read that and know there's more detail and answers coming, so just relax, and look forward.There are nice plots and Python code snippets throughout. It's helpful that each chapter ends with a summary and list of references.A lot of times we hear "supervised" or "unsupervised". It's nice that the book has three chapters on "semi-supervised" learning. In the Graph-Based Semi-Supervised Learning chapter, there is a "t-distributed stochastic neighbor embedding" (t-SNE) example, which is a topic I was curious about.

Amazon Verified review

Alfred H. Mar 06, 2020

I look at algorithms like appliances in a department store. Each has a particular use-case and purpose. For instance, depending on your individual needs while shopping for appliances most often stop to glance at the specs then at the cost to see if a particular unit suits their needs.Few ever think to build the appliance from scratch but rather by one already made to serve their needs progressively adopting it as an augmentation to everyday life. Even easier is when these items can be cataloged in one place, shopped, and put into use immediately (think Sears catalog of 1888). In its first catalog, Sears sold jewelry and watches. The directories grew in popularity, and with time different products were added and tested, even whole houses!While algorithms are not a new thing, thanks to the father of algebra: Abdullah Muhammad bin Musa al-Khwarizmi, it should NOT be challenging to catalog them. This book one of the best, like it, does that facilitating faster solutioning to get to the point of solving problems using built appliances (machine intelligence: algorithms).

Amazon Verified review

Thom Ives, Ph.D. May 26, 2020

Our exciting field of data science (DS) is exploding, and it’s hard to keep up with all of it. We each become extremely focused on our specific work in our current roles, and we are each funneled into specialized areas to solve specific challenges. After a long battle to deliver your great DS tool to production, your next challenge arrives. It seems this problem will require a DS method that you haven’t used since college. Maybe you’re not even sure, which DS method will best address your problem. Regardless, once you decide on a method of modeling, you have limited time to master it. You still need to collect, refine, and condition your data for that method. You must also master how to fight through the training of your chosen model. You’d seek help from your fellow DS’s, but they’re in the situation you just left.Now enters Giuseppe Bonaccorso - a DS friend with a corpus of DS methods that provide adequate mathematical overviews, explanations, and python code applied to substantial examples to get you up to speed quickly. I believe in keeping multiple sources at hand for learning / reviewing any methods. In that spirit, I'm relieved to have Giuseppe's book in my library. In a mere 750+ pages, he takes you on a tour of important methods that, while they won't make you an expert in the foundational mathematics for each method, they won't leave you blind in those respects either. I especially appreciate the references to foundational papers for each method following every group of methods for when we might need or want to go deeper. If you estimate that your library could be enhanced by such a substantial book as I've described, I believe you'd be well benefited by Giuseppe's book. He even manages to provide a good review of reinforced learning that, in my opinion, is extremely challenging to teach clearly.An important note is that this book does not cover natural language processing. In my experience, NLP would require another 750+ pages if it were to be covered as well as the methods that Giuseppe has covered. However, this is the only major field that he skips, and he does relate areas of that field to the techniques that he covers.

Amazon Verified review

TD59 Jun 13, 2021

I have used this book (1st and 2nd edition) in my Machine Learning class for a couple of years. Together with the Raschka and Mirjalili book (Python Machine Learning), Bonnacorso's provides a solid foundation for understanding the key algorithms, how they work, and how to fine-tune them. Both amanuals re required in my class and my students have only had positive feedback about both books. They are complementary as Bonnacorso does not cover some topics such as Natural Language Processing compared with Raschka, for instance.

Amazon Verified review

Duubar Villalobos Feb 19, 2020

As Giuseppe Bonaccorso expresses in this book, "It's always possible to join scientific rigor with an artistic approach." This book offers a great resource to dust off those concepts for the advance practitioners or to learn the fundamentals for those willing to master their ML algorithms artistically.I've been reading this book for over a week by now, and I like how Giuseppe explains important theoretical concepts related to machine learning models, bias, variance, overfitting, underfitting, data normalization, scaling, and so on.Even though this book requires a solid knowledge of essential machine learning topics and familiarity with Python programming language, don't be discouraged. I found out that this book provides the best first-hand experiential advice that someone can provide for someone willing to learn. Moreover, given the complexity of some subjects, proper mathematical training is desirable, but the willingness to learn outweighs it.If you are looking to get great advice and insights from an expert, this book is for you. My recommendation is not to rush over the concepts. As I am reading --still not finished since it's 800 pages, it makes me reflect on my problem-solving styles and helps me identify areas of opportunity for improvement.I have to thank Giuseppe for taking his time to think, sort his thoughts, write, and for sharing his knowledge and experiences for us to become better practitioners.

Amazon Verified review

FAQs

What is the delivery time and cost of print book?

Shipping Details

USA:

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.

Unfortunately, due to several restrictions, we are unable to ship to the following countries:

Afghanistan
American Samoa
Belarus
Brunei Darussalam
Central African Republic
The Democratic Republic of Congo
Eritrea
Guinea-bissau
Iran
Lebanon
Libiya Arab Jamahriya
Somalia
Sudan
Russian Federation
Syrian Arab Republic
Ukraine
Venezuela

What is custom duty/charge?

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order?

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges?

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.

How can I cancel my order?

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy?

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged?

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use?

You can pay with the following card types:

Visa Debit
Visa Credit
MasterCard
PayPal

What is the delivery time and cost of print books?

Shipping Details

USA:

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Unfortunately, due to several restrictions, we are unable to ship to the following countries:

Afghanistan
American Samoa
Belarus
Brunei Darussalam
Central African Republic
The Democratic Republic of Congo
Eritrea
Guinea-bissau
Iran
Lebanon
Libiya Arab Jamahriya
Somalia
Sudan
Russian Federation
Syrian Arab Republic
Ukraine
Venezuela

Mastering Machine Learning Algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work , Second Edition

What do you get with Print?