Michael Bowles - Machine Learning in Python : Essential Techniques for Predictive Analysis read book EPUB, PDF
9781118961742 English 1118961749 Learn a simpler and more effective way to analyze data andpredict outcomes with Python Machine Learning in Python shows you how to successfullyanalyze data using only two core machine learning algorithms, andhow to apply them using Python. By focusing on two algorithmfamilies that effectively predict outcomes, this book is able toprovide full descriptions of the mechanisms at work, and theexamples that illustrate the machinery with specific, hackablecode. The algorithms are explained in simple terms with no complexmath and applied using Python, with guidance on algorithmselection, data preparation, and using the trained models inpractice. You will learn a core set of Python programmingtechniques, various methods of building predictive models, and howto measure the performance of each model to ensure that the rightone is used. The chapters on penalized linear regression andensemble methods dive deep into each of the algorithms, and you canuse the sample code in the book to develop your own data analysissolutions. Machine learning algorithms are at the core of data analyticsand visualization. In the past, these methods required a deepbackground in math and statistics, often in combination with thespecialized R programming language. This book demonstrates howmachine learning can be implemented using the more widely used andaccessible Python programming language. Predict outcomes using linear and ensemble algorithmfamilies Build predictive models that solve a range of simple andcomplex problems Apply core machine learning algorithms using Python Use sample code directly to build custom solutions Machine learning doesnt have to be complex and highlyspecialized. Python makes this technology more accessible to a muchwider audience, using methods that are simpler, effective, and welltested. Machine Learning in Python shows you how to do this,without requiring an extensive background in math orstatistics., This book shows readers how they can successfully analyze data using only two core machine learning algorithms---and how to do so using the popular Python programming language. These algorithms deal with common scenarios faced by all data analysts and data scientists.This book focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers a multitude of use cases (what ad to place on a web page, predicting prices in securities markets, detecting credit card fraud, etc.). The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code. The author will explain in simple terms, using no complex math, how these algorithms work, and will then show how to apply them in Python. He will also provide advice on how to select from among these algorithms, and will show how to prepare the data, and how to use the trained models in practice.The author begins with an overview of the two core algorithms, explaining the types of problems solved by each one. He then introduces a core set of Python programming techniques that can be used to apply these algorithms. The author shows various techniques for building predictive models that solve a range of problems, from simple to complex; he also shows how to measure the performance of each model to ensure you use the right one. The following chapters provide a deep dive into each of the two algorithms: penalized linear regression and ensemble methods. Chapters will show how to apply each algorithm in Python. Readers can directly use the sample code to build their own solutions.
9781118961742 English 1118961749 Learn a simpler and more effective way to analyze data andpredict outcomes with Python Machine Learning in Python shows you how to successfullyanalyze data using only two core machine learning algorithms, andhow to apply them using Python. By focusing on two algorithmfamilies that effectively predict outcomes, this book is able toprovide full descriptions of the mechanisms at work, and theexamples that illustrate the machinery with specific, hackablecode. The algorithms are explained in simple terms with no complexmath and applied using Python, with guidance on algorithmselection, data preparation, and using the trained models inpractice. You will learn a core set of Python programmingtechniques, various methods of building predictive models, and howto measure the performance of each model to ensure that the rightone is used. The chapters on penalized linear regression andensemble methods dive deep into each of the algorithms, and you canuse the sample code in the book to develop your own data analysissolutions. Machine learning algorithms are at the core of data analyticsand visualization. In the past, these methods required a deepbackground in math and statistics, often in combination with thespecialized R programming language. This book demonstrates howmachine learning can be implemented using the more widely used andaccessible Python programming language. Predict outcomes using linear and ensemble algorithmfamilies Build predictive models that solve a range of simple andcomplex problems Apply core machine learning algorithms using Python Use sample code directly to build custom solutions Machine learning doesnt have to be complex and highlyspecialized. Python makes this technology more accessible to a muchwider audience, using methods that are simpler, effective, and welltested. Machine Learning in Python shows you how to do this,without requiring an extensive background in math orstatistics., This book shows readers how they can successfully analyze data using only two core machine learning algorithms---and how to do so using the popular Python programming language. These algorithms deal with common scenarios faced by all data analysts and data scientists.This book focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers a multitude of use cases (what ad to place on a web page, predicting prices in securities markets, detecting credit card fraud, etc.). The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code. The author will explain in simple terms, using no complex math, how these algorithms work, and will then show how to apply them in Python. He will also provide advice on how to select from among these algorithms, and will show how to prepare the data, and how to use the trained models in practice.The author begins with an overview of the two core algorithms, explaining the types of problems solved by each one. He then introduces a core set of Python programming techniques that can be used to apply these algorithms. The author shows various techniques for building predictive models that solve a range of problems, from simple to complex; he also shows how to measure the performance of each model to ensure you use the right one. The following chapters provide a deep dive into each of the two algorithms: penalized linear regression and ensemble methods. Chapters will show how to apply each algorithm in Python. Readers can directly use the sample code to build their own solutions.