Owing to his vast expertise in this ield, I am conident that Sebastian's insights into the world of Machine Learning in Python will be invaluable to users of all. Paperback: pages; ebook available in site format, Epub, PDF Packt Sebastian Raschka's new book, Python Machine Learning, has just been released. of your data, pick up Python Machine Learning—whether you want start from scratch or want .. at tailamephyli.cf~zkolter/course/linalg/linalg_ notes. pdf.
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In this book, we'll continue where we left off in "Python Machine Learning" and implement deep learning algorithms in TensorFlow. [tailamephyli.cf81] Python Machine Learning Python Machine Learning Sebastian Raschka epub. Python Machine Learning Sebastian Raschka pdf download. Python. The "Python Machine Learning (1st edition)" book code repository and info Introduction to NumPy [PDF] [EPUB] [Code Notebook] Sebastian Raschka's new book, Python Machine Learning, has just been released. I got a.
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Python Machine Learning - Second Edition
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Python Machine Learning - Second Edition. Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. Are you sure you want to claim this product using a token? Sebastian Raschka, Vahid Mirjalili September Quick links: What do I get with a Packt subscription? What do I get with an eBook?
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What do I get with a Video? Frequently bought together. Learn more Add to cart. Statistics for Machine Learning. Paperback pages. Book Description Machine learning is eating the software world, and now deep learning is extending machine learning.
Table of Contents Chapter 1: Giving Computers the Ability to Learn from Data. Building intelligent machines to transform data into knowledge. Chapter 2: Artificial neurons — a brief glimpse into the early history of machine learning.
Implementing a perceptron learning algorithm in Python.
Adaptive linear neurons and the convergence of learning. Chapter 3: Maximum margin classification with support vector machines.
1. Learning Python
Chapter 4: Partitioning a dataset into separate training and test sets. Chapter 5: Compressing Data via Dimensionality Reduction.
Unsupervised dimensionality reduction via principal component analysis. Supervised data compression via linear discriminant analysis. Using kernel principal component analysis for nonlinear mappings. Chapter 6: Using k-fold cross-validation to assess model performance. Debugging algorithms with learning and validation curves. Chapter 7: Combining Different Models for Ensemble Learning. Bagging — building an ensemble of classifiers from bootstrap samples. Chapter 8: Applying Machine Learning to Sentiment Analysis.
Preparing the IMDb movie review data for text processing. Training a logistic regression model for document classification.
Working with bigger data — online algorithms and out-of-core learning. Chapter 9: Turning the movie review classifier into a web application. Chapter Implementing an ordinary least squares linear regression model. Evaluating the performance of linear regression models.
Turning a linear regression model into a curve — polynomial regression. Dealing with nonlinear relationships using random forests.
Working with Unlabeled Data — Clustering Analysis. Modeling complex functions with artificial neural networks. A few last words about the neural network implementation. Executing objects in a TensorFlow graph using their names. Implementing a deep convolutional neural network using TensorFlow. What You Will Learn Understand the key frameworks in data science, machine learning, and deep learning Harness the power of the latest Python open source libraries in machine learning Master machine learning techniques using challenging real-world data Master deep neural network implementation using the TensorFlow library Ask new questions of your data through machine learning models and neural networks Learn the mechanics of classification algorithms to implement the best tool for the job Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Delve deeper into textual and social media data using sentiment analysis.
Authors Sebastian Raschka. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. ISBN www. Zagade Cover Work Shantanu N. Zagade Foreword We live in the midst of a data deluge. According to recent estimates, 2. This is so much data that over 90 percent of the information that we store nowadays was generated in the past decade alone.
Unfortunately, most of this information cannot be used by humans.
Either the data is beyond the means of standard analytical methods, or it is simply too vast for our limited minds to even comprehend. Through Machine Learning, we enable computers to process, learn from, and draw actionable insights out of the otherwise impenetrable walls of big data. As modern pioneers in the brave new world of big data, it then behooves us to learn more about Machine Learning. What is Machine Learning and how does it work? How can I use Machine Learning to take a glimpse into the unknown, power my business, or just find out what the Internet at large thinks about my favorite movie?
All of this and more will be covered in the following chapters authored by my good friend and colleague, Sebastian Raschka.
Publications & Research
When away from taming my otherwise irascible pet dog, Sebastian has tirelessly devoted his free time to the open source Machine Learning community. Over the past several years, Sebastian has developed dozens of popular tutorials that cover topics in Machine Learning and data visualization in Python. He has also developed and contributed to several open source Python packages, several of which are now part of the core Python Machine Learning workflow.
Owing to his vast expertise in this field, I am confident that Sebastian's insights into the world of Machine Learning in Python will be invaluable to users of all experience levels. I wholeheartedly recommend this book to anyone looking to gain a broader and more practical understanding of Machine Learning.
Randal S. He has been ranked as the number one most influential data scientist on GitHub by Analytics Vidhya. He has a yearlong experience in Python programming and he has conducted several seminars on the practical applications of data science and machine learning.Detailed information on each approach make this book a valuable experience for beginners as well as experienced users of R. Artificial Intelligence. It was a real pleasure to meet and chat with so many readers of my book.
It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras. Quick links: Pavel Yosifovich. His current projects include developing a crop analysis tool as part of integrated pest management strategies in greenhouses. What is the best validation metric for multi-class classification?
Fast paced, concentrated introductions showing the quickest way to put the tool to work in the real world.