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Machine Learning Mastery With Python

Machine Learning Mastery With Python
Author: Jason Brownlee
Publisher: Machine Learning Mastery
Total Pages: 177
Release: 2016-04-08
Genre: Computers
ISBN:

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The Python ecosystem with scikit-learn and pandas is required for operational machine learning. Python is the rising platform for professional machine learning because you can use the same code to explore different models in R&D then deploy it directly to production. In this Ebook, learn exactly how to get started and apply machine learning using the Python ecosystem.


Deep Learning With Python

Deep Learning With Python
Author: Jason Brownlee
Publisher: Machine Learning Mastery
Total Pages: 266
Release: 2016-05-13
Genre: Computers
ISBN:

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Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.


Imbalanced Classification with Python

Imbalanced Classification with Python
Author: Jason Brownlee
Publisher: Machine Learning Mastery
Total Pages: 463
Release: 2020-01-14
Genre: Computers
ISBN:

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Imbalanced classification are those classification tasks where the distribution of examples across the classes is not equal. Cut through the equations, Greek letters, and confusion, and discover the specialized techniques data preparation techniques, learning algorithms, and performance metrics that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently develop robust models for your own imbalanced classification projects.


Better Deep Learning

Better Deep Learning
Author: Jason Brownlee
Publisher: Machine Learning Mastery
Total Pages: 575
Release: 2018-12-13
Genre: Computers
ISBN:

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Deep learning neural networks have become easy to define and fit, but are still hard to configure. Discover exactly how to improve the performance of deep learning neural network models on your predictive modeling projects. With clear explanations, standard Python libraries, and step-by-step tutorial lessons, you’ll discover how to better train your models, reduce overfitting, and make more accurate predictions.


Generative Adversarial Networks with Python

Generative Adversarial Networks with Python
Author: Jason Brownlee
Publisher: Machine Learning Mastery
Total Pages: 655
Release: 2019-07-11
Genre: Computers
ISBN:

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Step-by-step tutorials on generative adversarial networks in python for image synthesis and image translation.


Long Short-Term Memory Networks With Python

Long Short-Term Memory Networks With Python
Author: Jason Brownlee
Publisher: Machine Learning Mastery
Total Pages: 245
Release: 2017-07-20
Genre: Computers
ISBN:

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The Long Short-Term Memory network, or LSTM for short, is a type of recurrent neural network that achieves state-of-the-art results on challenging prediction problems. In this laser-focused Ebook, finally cut through the math, research papers and patchwork descriptions about LSTMs. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what LSTMs are, and how to develop a suite of LSTM models to get the most out of the method on your sequence prediction problems.


Data Preparation for Machine Learning

Data Preparation for Machine Learning
Author: Jason Brownlee
Publisher: Machine Learning Mastery
Total Pages: 398
Release: 2020-06-30
Genre: Computers
ISBN:

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Data preparation involves transforming raw data in to a form that can be modeled using machine learning algorithms. Cut through the equations, Greek letters, and confusion, and discover the specialized data preparation techniques that you need to know to get the most out of your data on your next project. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively prepare your data for predictive modeling with machine learning.


Python Machine Learning

Python Machine Learning
Author: Sebastian Raschka
Publisher: Packt Publishing Ltd
Total Pages: 455
Release: 2015-09-23
Genre: Computers
ISBN: 1783555149

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Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.


XGBoost With Python

XGBoost With Python
Author: Jason Brownlee
Publisher: Machine Learning Mastery
Total Pages: 117
Release: 2016-08-05
Genre: Computers
ISBN:

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XGBoost is the dominant technique for predictive modeling on regular data. The gradient boosting algorithm is the top technique on a wide range of predictive modeling problems, and XGBoost is the fastest implementation. When asked, the best machine learning competitors in the world recommend using XGBoost. In this Ebook, learn exactly how to get started and bring XGBoost to your own machine learning projects.


Introduction to Time Series Forecasting With Python

Introduction to Time Series Forecasting With Python
Author: Jason Brownlee
Publisher: Machine Learning Mastery
Total Pages: 359
Release: 2017-02-16
Genre: Mathematics
ISBN:

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Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this Ebook, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.