Mastering Machine Learning Algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition
Mastering Machine Learning Algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition
Overview
Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems
Key Features 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 Book 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 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 Who 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 Contents Machine Learning Model Fundamentals Loss functions and Regularization Introduction to Semi-Supervised Learning Advanced Semi-Supervised Classifiation Graph-based Semi-Supervised Learning Clustering and Unsupervised Models Advanced Clustering and Unsupervised Models Clustering and Unsupervised Models for Marketing Generalized Linear Models and Regression Introduction to Time-Series Analysis Bayesian Networks and Hidden Markov Models The EM Algorithm Component Analysis and Dimensionality Reduction Hebbian Learning Fundamentals of Ensemble Learning Advanced Boosting Algorithms Modeling Neural Networks Optimizing Neural Networks Deep Convolutional Networks Recurrent Neural Networks Auto-Encoders Introduction to Generative Adversarial NetworksDescription
Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems
Key Features 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 Book 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 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 Who 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 Contents Machine Learning Model Fundamentals Loss functions and Regularization Introduction to Semi-Supervised Learning Advanced Semi-Supervised Classifiation Graph-based Semi-Supervised Learning Clustering and Unsupervised Models Advanced Clustering and Unsupervised Models Clustering and Unsupervised Models for Marketing Generalized Linear Models and Regression Introduction to Time-Series Analysis Bayesian Networks and Hidden Markov Models The EM Algorithm Component Analysis and Dimensionality Reduction Hebbian Learning Fundamentals of Ensemble Learning Advanced Boosting Algorithms Modeling Neural Networks Optimizing Neural Networks Deep Convolutional Networks Recurrent Neural Networks Auto-Encoders Introduction to Generative Adversarial NetworksCondition Guidelines
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- New: A brand-new copy with cover and original protective wrapping intact. Books with markings of any kind on the cover or pages, books marked as "Bargain" or "Remainder," or with any other labels attached, may not be listed as New condition.
- Used - Good: All pages and cover are intact (including the dust cover, if applicable). Spine may show signs of wear. Pages may include limited notes and highlighting. May include "From the library of" labels. Shrink wrap, dust covers, or boxed set case may be missing. Item may be missing bundled media.
- Used - Acceptable: All pages and the cover are intact, but shrink wrap, dust covers, or boxed set case may be missing. Pages may include limited notes, highlighting, or minor water damage but the text is readable. Item may but the dust cover may be missing. Pages may include limited notes and highlighting, but the text cannot be obscured or unreadable.
Note: Some electronic material access codes are valid only for one user. For this reason, used books, including books listed in the Used – Like New condition, may not come with functional electronic material access codes.
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Estimated delivery times:
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- Expedited Shipping: 3-5 business days
Shipping method varies depending on what is being shipped.
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If your package has been delivered in a PO Box, please note that we are not responsible for any damage that may result (consequences of extreme temperatures, theft, etc.).
If you have any questions regarding shipping or want to know about the status of an order, please contact us or email to support@stevensbooks.com.
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You may return most items within 30 days of delivery for a full refund.
To be eligible for a return, your item must be unused and in the same condition that you received it. It must also be in the original packaging.
Several types of goods are exempt from being returned. Perishable goods such as food, flowers, newspapers or magazines cannot be returned. We also do not accept products that are intimate or sanitary goods, hazardous materials, or flammable liquids or gases.
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- Gift cards
- Downloadable software products
- Some health and personal care items
To complete your return, we require a tracking number, which shows the items which you already returned to us.
There are certain situations where only partial refunds are granted (if applicable)
- Book with obvious signs of use
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If the item is returned to us for any of the following reasons, a 15% restocking fee will be applied to your refund total and you will be asked to pay for return shipping:
- Item(s) no longer needed or wanted.
- Item(s) returned to us due to an invalid or incomplete address.
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You should expect to receive your refund within four weeks of giving your package to the return shipper, however, in many cases you will receive a refund more quickly. This time period includes the transit time for us to receive your return from the shipper (5 to 10 business days), the time it takes us to process your return once we receive it (3 to 5 business days), and the time it takes your bank to process our refund request (5 to 10 business days).
If you need to return an item, please Contact Us with your order number and details about the product you would like to return. We will respond quickly with instructions for how to return items from your order.
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We'll pay the return shipping costs if the return is a result of our error (you received an incorrect or defective item, etc.). In other cases, you will be responsible for paying for your own shipping costs for returning your item. Shipping costs are non-refundable. If you receive a refund, the cost of return shipping will be deducted from your refund.
Depending on where you live, the time it may take for your exchanged product to reach you, may vary.
If you are shipping an item over $75, you should consider using a trackable shipping service or purchasing shipping insurance. We don’t guarantee that we will receive your returned item.
Categories
Overview
Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems
Key Features 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 Book 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 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 Who 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 Contents Machine Learning Model Fundamentals Loss functions and Regularization Introduction to Semi-Supervised Learning Advanced Semi-Supervised Classifiation Graph-based Semi-Supervised Learning Clustering and Unsupervised Models Advanced Clustering and Unsupervised Models Clustering and Unsupervised Models for Marketing Generalized Linear Models and Regression Introduction to Time-Series Analysis Bayesian Networks and Hidden Markov Models The EM Algorithm Component Analysis and Dimensionality Reduction Hebbian Learning Fundamentals of Ensemble Learning Advanced Boosting Algorithms Modeling Neural Networks Optimizing Neural Networks Deep Convolutional Networks Recurrent Neural Networks Auto-Encoders Introduction to Generative Adversarial NetworksDescription
Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems
Key Features 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 Book 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 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 Who 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 Contents Machine Learning Model Fundamentals Loss functions and Regularization Introduction to Semi-Supervised Learning Advanced Semi-Supervised Classifiation Graph-based Semi-Supervised Learning Clustering and Unsupervised Models Advanced Clustering and Unsupervised Models Clustering and Unsupervised Models for Marketing Generalized Linear Models and Regression Introduction to Time-Series Analysis Bayesian Networks and Hidden Markov Models The EM Algorithm Component Analysis and Dimensionality Reduction Hebbian Learning Fundamentals of Ensemble Learning Advanced Boosting Algorithms Modeling Neural Networks Optimizing Neural Networks Deep Convolutional Networks Recurrent Neural Networks Auto-Encoders Introduction to Generative Adversarial NetworksCondition Guidelines
Books - New and Used
The following guidelines apply to books:
- New: A brand-new copy with cover and original protective wrapping intact. Books with markings of any kind on the cover or pages, books marked as "Bargain" or "Remainder," or with any other labels attached, may not be listed as New condition.
- Used - Good: All pages and cover are intact (including the dust cover, if applicable). Spine may show signs of wear. Pages may include limited notes and highlighting. May include "From the library of" labels. Shrink wrap, dust covers, or boxed set case may be missing. Item may be missing bundled media.
- Used - Acceptable: All pages and the cover are intact, but shrink wrap, dust covers, or boxed set case may be missing. Pages may include limited notes, highlighting, or minor water damage but the text is readable. Item may but the dust cover may be missing. Pages may include limited notes and highlighting, but the text cannot be obscured or unreadable.
Note: Some electronic material access codes are valid only for one user. For this reason, used books, including books listed in the Used – Like New condition, may not come with functional electronic material access codes.
Shipping & Delivery
Shipping Fees
- Stevens Books offers FREE SHIPPING everywhere in the United States for ALL non-book orders, and $3.99 for each book.
- Packages are shipped from Monday to Friday.
- No additional fees and charges.
Delivery Times
The usual time for processing an order is 24 hours (1 business day), but may vary depending on the availability of products ordered. This period excludes delivery times, which depend on your geographic location.
Estimated delivery times:
- Standard Shipping: 5-8 business days
- Expedited Shipping: 3-5 business days
Shipping method varies depending on what is being shipped.
Tracking
All orders are shipped with a tracking number. Once your order has left our warehouse, a confirmation e-mail with a tracking number will be sent to you. You will be able to track your package at all times.
Damaged Parcel
If your package has been delivered in a PO Box, please note that we are not responsible for any damage that may result (consequences of extreme temperatures, theft, etc.).
If you have any questions regarding shipping or want to know about the status of an order, please contact us or email to support@stevensbooks.com.
Returns & Refunds
You may return most items within 30 days of delivery for a full refund.
To be eligible for a return, your item must be unused and in the same condition that you received it. It must also be in the original packaging.
Several types of goods are exempt from being returned. Perishable goods such as food, flowers, newspapers or magazines cannot be returned. We also do not accept products that are intimate or sanitary goods, hazardous materials, or flammable liquids or gases.
Additional non-returnable items:
- Gift cards
- Downloadable software products
- Some health and personal care items
To complete your return, we require a tracking number, which shows the items which you already returned to us.
There are certain situations where only partial refunds are granted (if applicable)
- Book with obvious signs of use
- CD, DVD, VHS tape, software, video game, cassette tape, or vinyl record that has been opened
- Any item not in its original condition, is damaged or missing parts for reasons not due to our error
- Any item that is returned more than 30 days after delivery
Items returned to us as a result of our error will receive a full refund,some returns may be subject to a restocking fee of 7% of the total item price, please contact a customer care team member to see if your return is subject. Returns that arrived on time and were as described are subject to a restocking fee.
Items returned to us that were not the result of our error, including items returned to us due to an invalid or incomplete address, will be refunded the original item price less our standard restocking fees.
If the item is returned to us for any of the following reasons, a 15% restocking fee will be applied to your refund total and you will be asked to pay for return shipping:
- Item(s) no longer needed or wanted.
- Item(s) returned to us due to an invalid or incomplete address.
- Item(s) returned to us that were not a result of our error.
You should expect to receive your refund within four weeks of giving your package to the return shipper, however, in many cases you will receive a refund more quickly. This time period includes the transit time for us to receive your return from the shipper (5 to 10 business days), the time it takes us to process your return once we receive it (3 to 5 business days), and the time it takes your bank to process our refund request (5 to 10 business days).
If you need to return an item, please Contact Us with your order number and details about the product you would like to return. We will respond quickly with instructions for how to return items from your order.
Shipping Cost
We'll pay the return shipping costs if the return is a result of our error (you received an incorrect or defective item, etc.). In other cases, you will be responsible for paying for your own shipping costs for returning your item. Shipping costs are non-refundable. If you receive a refund, the cost of return shipping will be deducted from your refund.
Depending on where you live, the time it may take for your exchanged product to reach you, may vary.
If you are shipping an item over $75, you should consider using a trackable shipping service or purchasing shipping insurance. We don’t guarantee that we will receive your returned item.
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