Introduction To Machine Learning With Python

Author: Andreas C. Müller
Publisher: "O'Reilly Media, Inc."
ISBN: 1449369898
Size: 37.88 MB
Format: PDF, ePub, Mobi
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Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills

Introduction To Machine Learning With Python

Author: David James
Publisher: Createspace Independent Publishing Platform
ISBN: 9781726230872
Size: 19.65 MB
Format: PDF, Kindle
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***** BUY NOW (will soon return to 24.78 $)******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning more about Machine Learning using Python? (For Beginners) 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/

Introduction To Machine Learning With Python

Author: Daniel Nedal
Publisher: Createspace Independent Publishing Platform
ISBN: 9781724417503
Size: 48.40 MB
Format: PDF, ePub, Docs
View: 4574
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******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/

Python Machine Learning

Author: Sebastian Raschka
Publisher: Packt Publishing Ltd
ISBN: 1783555149
Size: 16.41 MB
Format: PDF
View: 6616
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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.

Machine Learning Python

Author: Nexcod Publishing
Publisher: Independently Published
ISBN: 9781078103459
Size: 66.67 MB
Format: PDF, ePub, Mobi
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Python Machine learningPython is a general-purpose high level programming language that is being increasingly used in data science and in designing machine learning algorithms. This tutorial provides a quick introduction to Python and its libraries like numpy, scipy, pandas, matplotlib and explains how it can be applied to develop machine learning algorithms that solve real world problems. This tutorial starts with an introduction to machine learning and the Python language and shows you how to setup Python and its packages. It further covers all important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. This tutorial also provides various projects that teaches you the techniques and functionalities such as news topic classification, spam email detection, online ad click-through prediction, stock prices forecast and other several important machine learning algorithms.This tutorial has been prepared for professionals aspiring to learn the basics of Python and develop applications involving machine learning techniques such as recommendation, classification, and clustering. Through this tutorial, you will learn to solve data-driven problems and implement your solutions using the powerful yet simple programming language, Python and its packages. After completing this tutorial, you will gain a broad picture of the machine learning environment and the best practices for machine learning techniques.

Python Data Analytics

Author: tony f. charles
Publisher: Independently Published
ISBN: 9781712916803
Size: 22.26 MB
Format: PDF, Docs
View: 6355
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Are you looking for a beginners guide? Do you want to learn how to use python for beginners in a simple way? Do you want to enter into the new world of Python for beginners in an efficient and effective way? This book will teach you the basics as well as the advanced concepts of computers and programming. The gaming industry is growing rapidly and Python offers a lot of libraries to create games. Many tech giants rely on Python to deliver world-class applications. In This book you will learn: Basics of Python for Data Analysis NumPy 2-D and 3-D arrays SciPy Linear Algebra Pandas Operations Python IDE Atom Eclipse Variables and Data Types Decision Making and Basic Operators Object Oriented Programming Regular Expressions Data Handling Load date from different server such as CSV, URL or SQL Python Aggregation Building Machine Learning Models Data Science Data Pipelines Data Segregation Importance of Metadata Machine Learning Algorithms Scikit Learn Effective Data Visualization Evaluating Accuracy of the Model Advantages of Naïve Bayes K-Means Clustering Expectation-Minimization Algorithm Mean Shift Algorithm Artificial Neural Networks Deep Neural Networks Architecture of ANN's Data Science in Real World Virtual Assistants Risk and Fraud Detection Data Analytics in Detail Types and Categories of Data Analytics Steps in Data Mining Data Science Lifecycle and Model Building Improving Data Science Models Determine Problems Search for More Data Deep Learning and Business Model Interpretability Autonomous Vehicles Finding Useful DataBig Data This book is not just a startup guide. This book will prove beneficial for years to come. The book has the latest codes and techniques so you can equip your skills according to the current market challenges. After all, the purpose is to land a nicely paid job in a globally recognized firm. This book will help you reach that goal! Most people can learn how to code but not just anyone can code smartly. This book is going to help you to think out of the box and take on problems with a completely different perspective. The tricks mentioned will make you invaluable to any software development firm. Even if you don't have any skills this book help you step by step to achieve your goal in a few days you will be able to learn it. scroll up and buy now

Python For Beginners

Author: Josh Hugh Learning
Publisher: Independently Published
ISBN: 9781692298418
Size: 36.89 MB
Format: PDF, ePub
View: 3927
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If you Want to Learn the Python Programming Basis in a Little Time and in a Nice Way Without Efforts, This guide is for you › › › keep reading › › › Python is a very used and versatile programming language, used by famous and important sites like Youtube and Facebook. With this language you can finally program apps, games, softwares and much more. That's the reason why programmers that are able to master this language are requested in the world of work. But approaching a new programming language is not easy because you will have to learn new functions and new syntaxes to remember, especially if you are a beginner. ★ If you are a beginner, this book is perfect for you, because you will have a simple and comprehensive guide that will help you in your educational path. This guidebook is going to explore the Python coding language, one of the best options for coding out there to help you to start writing your own codes in no time. This guidebook is going to provide you with all of the tools that you need to finally make this work for you and to help you write your own codes in no time at all. ★ This book was designed to make your job easier and faster, written to make content easier to understand and memorize. You will learn: The Python language Data and variables Control flow tools The files A look at the classes Creating your own modules The regular expressions Networking The process of multithreading The way to access databases with Python How GUI programming works More about machine learning and how it works with Python Working with Python is one of the best decisions you can make when working on your journey into coding. When you are ready to learn how to make this your own journey and how to do some of the best codings ever in no time at all, scroll the top of the page and select »» BUY NOW ««. ✓ Josh Hugh Learning

Python Machine Learning For Beginners

Author: Leonard Deep
Publisher:
ISBN: 9781097858309
Size: 51.30 MB
Format: PDF, Docs
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Are you interested to get into the programming world? Do you want to learn and understand Python and Machine Learning? Python Machine Learning for Beginners is the guide for you. Python Machine Learning for Beginners is the ultimate guide for beginners looking to learn and understand how Python programming works. Python Machine Learning for Beginners is split up into easy to learn chapters that will help guide the readers through the early stages of Python programming. It's this thought out and systematic approach to learning which makes Python Machine Learning for Beginners such a sought-after resource for those that want to learn about Python programming and about Machine Learning using an object-oriented programming approach. Inside Python Machine Learning for Beginners you will discover: An introduction to Machine Learning The main concepts of Machine Learning The basics of Python for beginners Machine Learning with Python Data Processing, Analysis, and Visualizations Case studies and much more! Throughout the book, you will learn the basic concepts behind Python programming which is designed to introduce you to Python programming. You will learn about getting started, the keywords and statements, data types and type conversion. Along with different examples, there are also exercises to help ensure that the information sinks in. You will find this book an invaluable tool for starting and mastering Machine Learning using Python. Once you complete Python Machine Learning for Beginners, you will be more than prepared to take on any Python programming. Scroll back up to the top of this page and hit BUY IT NOW to get your copy of Python Machine Learning for Beginners! You won't regret it!

Python For Data Science

Author: tony f. charles
Publisher: Independently Published
ISBN: 9781654509811
Size: 43.15 MB
Format: PDF
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Are you looking for a beginners guide? Do you want to learn how to use python for beginners in a simple way? Do you want to enter into the new world of Python for beginners in an efficient and effective way? This book will teach you the basics as well as the advanced concepts of computers and programming. The gaming industry is growing rapidly and Python offers a lot of libraries to create games. Many tech giants rely on Python to deliver world-class applications. In This book you will learn: Python setup Anaconda Winpython Data science packeges Jupyter Data munging with pandas The process Importing datasets Data preprocessing The data science pipeline Principal component analysis Supervised learning algorithms Analyzing big data Neural networks structures Classification and regression trees The overfitting problem New features Naïve bayes classifier Linear regression Logistic regression Support vector machine Applications in the real world Pruning Data selection This book is not just a startup guide. This book will prove beneficial for years to come. The book has the latest codes and techniques so you can equip your skills according to the current market challenges. After all, the purpose is to land a nicely paid job in a globally recognized firm. This book will help you reach that goal! Most people can learn how to code but not just anyone can code smartly. This book is going to help you to think out of the box and take on problems with a completely different perspective. The tricks mentioned will make you invaluable to any software development firm. Even if you don't have any skills this book help you step by step to achieve your goal in a few days you will be able to learn it. scroll up and buy now

Machine Learning With Python

Author: Mark J. Branson
Publisher: Independently Published
ISBN: 9781712506578
Size: 49.65 MB
Format: PDF, Mobi
View: 696
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This book explicitly gives the reader layman's introduction to machine learning with implementation in python libraries particularly using scikit learn and Tensor flow. We will learn about machine learning and its subset deep learning in detail along with program codes that will give a good overview for the developers. We will also discuss in detail about different machine learning algorithms like support vector machine, Linear regression method in detail with python examples. In the second part of the book, we will deal with neural networks and implement them using Tensor Flow. This book is easily understood and deals with complex concepts explained in a simple way such that beginners can understand it easily. Here we describe the most important topics explained in the book in no particular order: - A brief introduction to machine learning with a small known history and terminology that is closely related to machine learning. - We will then give a brief project structure of machine learning that can be used to understand the process that goes on with a data science project. - Then the book describes in detail about regularization and how to fit a model into the data. - In the next chapter, we will deal with gradient descent and optimization with python implementation. - We will then learn about feature engineering, data preprocessing methods, cross-validation, and hyperparameter tuning in detail with python code implementation. - The last section of the first part deals with machine learning algorithms and their implementation in detail. - The second part starts with a brief introduction to neural networks and neurons - The next two chapters will help us understand the complexity and importance of neural networks. We will also build a neural network using python in this chapter. - The last chapter deals with huge data sets like webpages. We will introduce page ranking algorithm and its simplicity. What are you waiting for? BUY NOW this machine learning book for data science.