Practical Machine Learning With R And Python Second Edition
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Practical Machine Learning With R And Python Second Edition
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Author : Tinniam V. Ganesh
language : en
Publisher:
Release Date : 2018-05-30
Practical Machine Learning With R And Python Second Edition written by Tinniam V. Ganesh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-30 with categories.
This is the 2nd edition of the book. This 2nd edition includes more content, detailed code comments and better formatting for readbility. This book implements many common Machine Learning algorithms in equivalent R and Python. The book touches on R and Python implementations of different regression models, classification algorithms including logistic regression, KNN classification, SVMs, b-splines, random forest, boosting etc. Other techniques like best-fit, forward fit, backward fit, and lasso and ridge regression are also covered. The book further touches on classification metrics for computing accuracy, recall, precision etc. There are implementations of validation, ROC and AUC curves in both R and Python. Finally, the book covers unsupervised learning methods like K-Means, PCA and Hierarchical clustering.The book is well suited for the novice and the expert. The first two chapters discuss the most important programming constructs in R and Python. The third chapter highlights equivalent programming phrases in R and Python. Hence, those with no knowledge of R and Python will find these introductory chapters useful. Those who are proficient in one of the language can further their knowledge on the other. Those are familiar with both R and Python will find the equivalent implementations useful to internalize the algorithms. This book should serve as a useful and handy reference for Machine Learning algorithms in both R and Python
Practical Machine Learning With R And Python Third Edition
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Author : Tinniam V. Ganesh
language : en
Publisher:
Release Date : 2019
Practical Machine Learning With R And Python Third Edition written by Tinniam V. Ganesh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
This is the 3rd edition of the book. All the code sections are formatted with fixed-width font Consolas for better readability. This book implements many common Machine Learning algorithms in equivalent R and Python. The book touches on R and Python implementations of different regression models, classification algorithms including logistic regression, KNN classification, SVMs, b-splines, random forest, boosting etc. Other techniques like best-fit, forward fit, backward fit, and lasso and ridge regression are also covered. The book further touches on classification metrics for computing accuracy, recall, precision etc. There are implementations of validation, ROC and AUC curves in both R and Python. Finally, the book covers unsupervised learning methods like K-Means, PCA and Hierarchical clustering.The book is well suited for the novice and the expert. The first two chapters discuss the most important programming constructs in R and Python. The third chapter highlights equivalent programming phrases in R and Python. Hence, those with no knowledge of R and Python will find these introductory chapters useful. Those who are proficient in one of the language can further their knowledge on the other. Those are familiar with both R and Python will find the equivalent implementations useful to internalize the algorithms. This book should serve as a useful and handy reference for Machine Learning algorithms in both R and Python
Practical Deep Learning 2nd Edition
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Author : Ronald T. Kneusel
language : en
Publisher: NO STARCH PRESS, INC
Release Date : 2025-07-08
Practical Deep Learning 2nd Edition written by Ronald T. Kneusel and has been published by NO STARCH PRESS, INC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-08 with Computers categories.
Deep learning made simple. Dip into deep learning without drowning in theory with this fully updated edition of Practical Deep Learning from experienced author and AI expert Ronald T. Kneusel. After a brief review of basic math and coding principles, you’ll dive into hands-on experiments and learn to build working models for everything from image analysis to creative writing, and gain a thorough understanding of how each technique works under the hood. Whether you’re a developer looking to add AI to your toolkit or a student seeking practical machine learning skills, this book will teach you: How neural networks work and how they’re trained How to use classical machine learning models How to develop a deep learning model from scratch How to evaluate models with industry-standard metrics How to create your own generative AI models Each chapter emphasizes practical skill development and experimentation, building to a case study that incorporates everything you’ve learned to classify audio recordings. Examples of working code you can easily run and modify are provided, and all code is freely available on GitHub. With Practical Deep Learning, second edition, you’ll gain the skills and confidence you need to build real AI systems that solve real problems. New to this edition: Material on computer vision, fine-tuning and transfer learning, localization, self-supervised learning, generative AI for novel image creation, and large language models for in-context learning, semantic search, and retrieval-augmented generation (RAG).
Practical Machine Learning With R And Python
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Author : Tinniam V. Ganesh
language : en
Publisher:
Release Date : 2017-12-02
Practical Machine Learning With R And Python written by Tinniam V. Ganesh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-02 with categories.
This book implements many common Machine Learning algorithms in equivalent R and Python. The book touches on R and Python implementations of different regression models, classification algorithms including logistic regression, KNN classification, SVMs, b-splines, random forest, boosting etc. Other techniques like best-fit, forward fit, backward fit, and lasso and ridge regression are also covered. The book further touches on classification metrics for computing accuracy, recall, precision etc. There are implementations of validation, ROC and AUC curves in both R and Python. Finally, the book covers unsupervised learning methods like K-Means, PCA and Hierarchical clustering.The book is well suited for the novice and the expert. The first two chapters discuss the most important programming constructs in R and Python. The third chapter highlights equivalent programming phrases in R and Python. Hence, those with no knowledge of R and Python will find these introductory chapters useful. Those who are proficient in one of the language can further their knowledge on the other. Those are familiar with both R and Python will find the equivalent implementations useful to internalize the algorithms. This book should serve as a useful and handy reference for Machine Learning algorithms in both R and Python
Practical Machine Learning For Data Analysis Using Python
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Author : Abdulhamit Subasi
language : en
Publisher: Academic Press
Release Date : 2020-06-05
Practical Machine Learning For Data Analysis Using Python written by Abdulhamit Subasi and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-05 with Computers categories.
Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. - Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas - Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data - Explores important classification and regression algorithms as well as other machine learning techniques - Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features
Practical Machine Learning With R
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Author : Brindha Priyadarshini Jeyaraman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-08-30
Practical Machine Learning With R written by Brindha Priyadarshini Jeyaraman and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-30 with Computers categories.
Understand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problems Key FeaturesGain a comprehensive overview of different machine learning techniquesExplore various methods for selecting a particular algorithmImplement a machine learning project from problem definition through to the final modelBook Description With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it. What you will learnDefine a problem that can be solved by training a machine learning modelObtain, verify and clean data before transforming it into the correct format for usePerform exploratory analysis and extract features from dataBuild models for neural net, linear and non-linear regression, classification, and clusteringEvaluate the performance of a model with the right metricsImplement a classification problem using the neural net packageEmploy a decision tree using the random forest libraryWho this book is for If you are a data analyst, data scientist, or a business analyst who wants to understand the process of machine learning and apply it to a real dataset using R, this book is just what you need. Data scientists who use Python and want to implement their machine learning solutions using R will also find this book very useful. The book will also enable novice programmers to start their journey in data science. Basic knowledge of any programming language is all you need to get started.
Python Machine Learning By Example
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-02-28
Python Machine Learning By Example written by Yuxi (Hayden) Liu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-28 with Mathematics categories.
Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn Key FeaturesExploit the power of Python to explore the world of data mining and data analyticsDiscover machine learning algorithms to solve complex challenges faced by data scientists todayUse Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projectsBook 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 learnUnderstand the important concepts in machine learning and data scienceUse Python to explore the world of data mining and analyticsScale up model training using varied data complexities with Apache SparkDelve deep into text and NLP using Python libraries such NLTK and gensimSelect and build an ML model and evaluate and optimize its performanceImplement ML algorithms from scratch in Python, TensorFlow, and scikit-learnWho 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.
Python Machine Learning By Example
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-10-30
Python Machine Learning By Example written by Yuxi (Hayden) Liu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-30 with Computers categories.
A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques Key FeaturesDive into machine learning algorithms to solve the complex challenges faced by data scientists todayExplore cutting edge content reflecting deep learning and reinforcement learning developmentsUse updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-endBook Description Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems. What you will learnUnderstand the important concepts in ML and data scienceUse Python to explore the world of data mining and analyticsScale up model training using varied data complexities with Apache SparkDelve deep into text analysis and NLP using Python libraries such NLTK and GensimSelect and build an ML model and evaluate and optimize its performanceImplement ML algorithms from scratch in Python, TensorFlow 2, PyTorch, and scikit-learnWho this book is for If you’re a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary.
Machine Learning With Python
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Author : Oliver Theobald
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-03-06
Machine Learning With Python written by Oliver Theobald and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-06 with Computers categories.
Unlock the secrets of data science and machine learning with our comprehensive Python course, designed to take you from basics to complex algorithms effortlessly Key Features Navigate through Python's machine learning libraries effectively Learn exploratory data analysis and data scrubbing techniques Design and evaluate machine learning models with precision Book DescriptionThe course starts by setting the foundation with an introduction to machine learning, Python, and essential libraries, ensuring you grasp the basics before diving deeper. It then progresses through exploratory data analysis, data scrubbing, and pre-model algorithms, equipping you with the skills to understand and prepare your data for modeling. The journey continues with detailed walkthroughs on creating, evaluating, and optimizing machine learning models, covering key algorithms such as linear and logistic regression, support vector machines, k-nearest neighbors, and tree-based methods. Each section is designed to build upon the previous, reinforcing learning and application of concepts. Wrapping up, the course introduces the next steps, including an introduction to Python for newcomers, ensuring a comprehensive understanding of machine learning applications.What you will learn Analyze datasets for insights Scrub data for model readiness Understand key ML algorithms Design and validate models Apply Linear and Logistic Regression Utilize K-Nearest Neighbors and SVMs Who this book is for This course is ideal for aspiring data scientists and professionals looking to integrate machine learning into their workflows. A basic understanding of Python and statistics is beneficial.
Machine Learning Using R
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Author : Karthik Ramasubramanian
language : en
Publisher: Apress
Release Date : 2019-01-04
Machine Learning Using R written by Karthik Ramasubramanian and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-04 with Computers categories.
Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if you are only comfortable with R. As in the first edition, the authors have kept the fine balance of theory and application of machine learning through various real-world use-cases which gives you a comprehensive collection of topics in machine learning. New chapters in this edition cover time series models and deep learning. What You'll Learn Understand machine learning algorithms using R Master the process of building machine-learning models Cover the theoretical foundations of machine-learning algorithms See industry focused real-world use cases Tackle time series modeling in R Apply deep learning using Keras and TensorFlow in R Who This Book is For Data scientists, data science professionals, and researchers in academia who want to understand the nuances of machine-learning approaches/algorithms in practice using R.