Python Machine Learning

Author: Sebastian Raschka

Publisher: Packt Publishing Ltd

ISBN: 1783555149

Category: Computers

Page: 454

View: 8321

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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.
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Python Machine Learning

How to Learn Machine Learning with Python. The Complete Guide to Understand Python Machine Learning for Beginners and Artificial Intelligence

Author: Oliver Soranson

Publisher: Independently Published

ISBN: 9781706512110

Category:

Page: 162

View: 7184

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You must have gotten the opportunity to pay for parking at a mall, where a machine is able to tell the amount of money you owe depending on how long your car was in the parking lot and probably a few other features. However, have you ever wondered just how the parking meter is able to differentiate between currencies and give you the right change? Furthermore, have you ever wondered how applications such as Uber can predict the amount of time it will take you to get home to such a high degree of accuracy yet traffic can be so unpredictable? If you have ever asked yourself questions about the basic or especially the complex predictions and conclusions machines are making these days, then your answer lies in Machine learning. Human beings have different ways in which they learn, some of the methods including experience or even having someone teach them. Therefore, to try to make machines even more useful to human beings, it is possible to teach machines to make decisions in several ways, and these can learn and have faster and more accurate output compared to how a human being would compete. People usually understand the concept of how a machine will do something you have programmed it to do because people came to terms with that years ago. However, what still fascinates people is how a machine is able to make decisions independently by considering a situation and even making a prediction that turns out to be true. Machine learning is at a very high-level today when you compare to a few years back, so that would make you wonder just how advanced machines will be in the next 20 to 30 years. It is highly likely that machines will become better versions of us, but we hope they will never get so independent and intelligent that they eventually decide to rule over us. The objective of writing this book is to help a beginner to understand the basics of machine learning to the point of even training a machine to carry out some functions. This book also explains the advantages associated with using Python, since an individual does not necessarily have to be an expert coder to start using it. Some of the main topics discussed in this book include: The history of machine learning Key machine learning definitions Application of machine learning Key elements of machine learning Types of artificial intelligence learning Mathematical notation for machine learning Terminologies in use for machine learning Roadmap for building machine-learning systems Using python for machine learning (and understanding variables, essential operator, functions, conditional statements, and loop) Types of artificial neural networks Artificial neural network layers Advantages and disadvantages of neural networks Machine learning classification Types of classifiers in python machine learning Machine learning classification models Metrics for evaluating machine learning classification models Machine learning training model Developing a machine learning model with python Training simple machine learning algorithms for classification Building good training sets Would you like to know everything you need about Python Machine Learning? Download this book and commence your journey to learning how to understand Python Machine Learning for Beginners and Artificial Intelligence.
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Python Machine Learning

Understand Python Libraries (Keras, NumPy, Scikit-Lear, TensorFlow) for Implementing Machine Learning Models in Order to Build Intelligent Systems

Author: Ethem Mining

Publisher: Independently Published

ISBN: 9781671257900

Category:

Page: 245

View: 4062

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Do you want to learn how to apply efficiently your Python knowledge to implement learning models? Do you want to understand which ones are the best libraries to use and why is Python considered the best language for machine learning? What do you need to learn to move from being a complete beginner to someone with advanced knowledge of machine learning? Tech is slowly moving towards high-level automation, robotics, machine learning, artificial intelligence, big data and other high level computing concepts. That's why self-driving cars, customized product recommendations, real time pricing, facial recognition, retargeting ads, geo-targeting, using bots for customer service and much more is a thing these days. So if you ever want to leverage the full power of any of these advanced computing concepts, now is the right time to get in! So where do you even start? Well, my recommendation is to start by learning machine learning, as that will effectively help you to understand the ins and outs of how to build intelligent systems. The book will teach you: The basics about machine learning, including what it is, how it developed, the place of big data in machine learning as well as how machine learning works How machine learning works in 7 simple steps How machine learning is applied in real world situations like health care, customer service, underwriting, real time pricing, self-driving cars, fraud detection, robotics, facial recognition, product recommendations, retargeting customers and much more How supervised learning is a thing in machine learning, including the types of supervised learning, feature vectors, how to pick the learning algorithm and more How to leverage the power of unsupervised machine learning, including what unsupervised learning means, how to use different approaches to clustering and, visualization How you can use semi-supervised learning as well as reinforcement based learning, where both of them are used and more The place of regression techniques in machine learning, including the different regression methods that you can use as well as how to use them well How data is classified in machine learning, including the different methods of classifying data How to unleash the full power of neural networks in machine learning while leveraging the power of different libraries like TensorFlow, Keras and more Multiple ways to access computing power in machine learning How to unleash the full power of data mining using different libraries like The Scikit-Learn How to make the most use of NumPy Ndarray for high-level operations and in neural networks And much more! Even if this is your first encounter with the machine learning and want to dip your feet into the world of high level computing concepts like machine learning, deep learning, artificial intelligence and more, this book will break everything using easy to follow language to help you to apply what you learn right away! Would You Like To Know More? Click Buy Now With 1-Click or Buy Now to get started!
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Python Machine Learning from Scratch

Step-By-Step Guide with Scikit-Learn and TensorFlow

Author: Daniel Nedal

Publisher: Createspace Independent Publishing Platform

ISBN: 9781724264374

Category:

Page: 130

View: 1957

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***BUY NOW (Will soon return to 20.59) ******Free eBook for customers who purchase the print book from Amazon*** Are you thinking of learning more about Machine Learning using Python? This book would seek to explain common terms and algorithms in an intuitive way. The author used a progressive approach whereby we start out slowly and improve on the complexity of our solutions. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples This book and the accompanying examples, you would be well suited to tackle problems which pique your interests using machine learning. Instead of tough math formulas, this book contains several graphs and images which detail all important Machine Learning concepts and their applications. Target Users The book designed for a variety of target audiences. The most suitable users would include: Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field. Software developers and engineers with a strong programming background but seeking to break into the field of machine learning. Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird's eye view of current techniques and approaches. What's Inside This Book? Supervised Learning Algorithms Unsupervised Learning Algorithms Semi-supervised Learning Algorithms Reinforcement Learning Algorithms Overfitting and underfitting correctness The Bias-Variance Trade-off Feature Extraction and Selection A Regression Example: Predicting Boston Housing Prices Import Libraries: How to forecast and Predict Popular Classification Algorithms Introduction to K Nearest Neighbors Introduction to Support Vector Machine Example of Clustering Running K-means with Scikit-Learn Introduction to Deep Learning using TensorFlow Deep Learning Compared to Other Machine Learning Approaches Applications of Deep Learning How to run the Neural Network using TensorFlow Cases of Study with Real Data Sources & References Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: If you want to smash Machine Learning from scratch, this book is for you. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Does this book include everything I need to become a Machine Learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected] If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by the data scientist Alain Kaufmann at http: //aisciences.net/free-books/
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Python Machine Learning

A Step-by-Step Guide to Scikit-Learn and TensorFlow (Includes a Python Programming Crash Course)

Author: Konnor Cluster

Publisher: N.A

ISBN: 9781705835951

Category:

Page: 146

View: 8608

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If you need to learn how to use the Python Programming Language to implement your own Machine Learning solution, and you are searching for a reference to start from, then keep reading. Machine learning represents now the most interesting, performing and growing technology among all technologies related to Artificial Intelligence and represents also an incredible opportunity if you want to grow your business or if you are searching for a new job, but it's not very easy to understand how it works. Sometimes, even the most seasoned and skilled engineers are scared to approach this new topic. This book can assist you in understanding How machine learning works How to work with Python How to set up and run a machine learning solution on your home computer How to use two of the most popular Machine Learning libraries Even if you have not a degree in computer science or math, even if you have never worked with Machine Learning or Python, with this book you can understand how to benefit from this set of technologies and unlock their extraordinary potential. If you want to enter into the exciting world of Machine Learning, scroll up and click the "buy now" button!
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Python Machine Learning

A Practical Beginner's Guide for Understanding Machine Learning, Deep Learning and Neural Networks with Python, Scikit-Learn, Tensorflow and Keras

Author: Brandon Railey

Publisher: Data Science

ISBN: 9783903331723

Category:

Page: 292

View: 3539

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Have you come across the terms machine learning and neural networks in most articles you have recently read? Do you also want to learn how to build a machine learning model that will answer your questions within a blink of your eyes? If you responded yes to any of the above questions, you have come to the right place.
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Python Machine Learning

A Hands-On Beginner's Guide to Effectively Understand Artificial Neural Networks and Machine Learning Using Python (With Tips and Tricks)

Author: James Deep

Publisher: N.A

ISBN: 9781705586686

Category:

Page: 131

View: 4346

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If you are ready to know the link between Python Programming and Machine Learning, then keep reading. The concept of Artificial Intelligence is regarded by many as the way of the future. It covers vast areas of study and complements almost every aspect of human life. The evolution of intelligent machines has been on the rise as experts try to outsmart each other in innovation. Many business models, health organizations, government units, and many more, have adopted one or two practices that incorporate automation into their daily activities. As part of Artificial Intelligence, Machine Learning has seen significant applications across significant sectors of the economies of the world. To date, almost every aspect of our life has elements of Machine Learning. From the phones we use to the retail stores we do our shopping at, the areas covered by Machine Learning applications are drastically increasing by the day. This technique has helped the developers of software to create high-tech apps that predict changes in the market, sort large amounts of data, and offer solutions to major real-world problems. As much the trends are evident on the ground, the theoretical perspective remains an isolated area to many. Scaling through Machine Learning requires some knowledge of programming. In general terms, you need a platform to gain an understanding of the topic and hone your skills in the same. Python Machine Learning covers the concept of Machine Learning, in a detailed but well elaborate language of presentation. The topic may not be simple but is very worthwhile as long as you understand the fundamental concepts that underlie Machine Learning. Inside this book you will find Types of ML Use of Python in Machine Learning Essential Libraries for ML Regression Analysis Decision Trees The Perceptron Random Forest Algorithms K-Nearest Neighbors (KNN) ...and many more amazing and interesting topics! This book takes readers on a knowledge trip through solved examples, tips, tricks, and visualized content. It will not only create an appetite for more but also give readers what they need to know about Machine Learning, all these in a small volume for easy reading. Want to know more? Scroll to the top of the page and click the "buy now" button!
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Python Machine Learning Cookbook

Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets, 2nd Edition

Author: Giuseppe Ciaburro,Prateek Joshi

Publisher: Packt Publishing Ltd

ISBN: 1789800757

Category: Computers

Page: 642

View: 3021

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Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch Key Features Learn and implement machine learning algorithms in a variety of real-life scenarios Cover a range of tasks catering to supervised, unsupervised and reinforcement learning techniques Find easy-to-follow code solutions for tackling common and not-so-common challenges Book Description This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples. What you will learn Use predictive modeling and apply it to real-world problems Explore data visualization techniques to interact with your data Learn how to build a recommendation engine Understand how to interact with text data and build models to analyze it Work with speech data and recognize spoken words using Hidden Markov Models Get well versed with reinforcement learning, automated ML, and transfer learning Work with image data and build systems for image recognition and biometric face recognition Use deep neural networks to build an optical character recognition system Who this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and Python programmers who want to solve real-world challenges using machine-learning techniques and algorithms. If you are facing challenges at work and want ready-to-use code solutions to cover key tasks in machine learning and the deep learning domain, then this book is what you need. Familiarity with Python programming and machine learning concepts will be useful.
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Python Machine Learning By Example

Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition

Author: Yuxi (Hayden) Liu

Publisher: Packt Publishing Ltd

ISBN: 1789617553

Category: Computers

Page: 382

View: 5164

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Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn Key Features Exploit the power of Python to explore the world of data mining and data analytics Discover machine learning algorithms to solve complex challenges faced by data scientists today Use Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects Book Description The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML. Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way. With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more. By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities. What you will learn Understand the important concepts in machine learning and data science Use Python to explore the world of data mining and analytics Scale up model training using varied data complexities with Apache Spark Delve deep into text and NLP using Python libraries such NLTK and gensim Select and build an ML model and evaluate and optimize its performance Implement ML algorithms from scratch in Python, TensorFlow, and scikit-learn Who this book is for If you’re a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.
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