Download or read book Applied Unsupervised Learning with Python written by Benjamin Johnston and published by Packt Publishing Ltd. This book was released on 2019-05-28 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data Key FeaturesLearn how to select the most suitable Python library to solve your problemCompare k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use themDelve into the applications of neural networks using real-world datasetsBook Description Unsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you on the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The course begins by explaining how basic clustering works to find similar data points in a set. Once you are well versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. You will complete the course by challenging yourself through various interesting activities such as performing a Market Basket Analysis and identifying relationships between different merchandises. By the end of this course, you will have the skills you need to confidently build your own models using Python. What you will learnUnderstand the basics and importance of clusteringBuild k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packagesExplore dimensionality reduction and its applicationsUse scikit-learn (sklearn) to implement and analyse principal component analysis (PCA)on the Iris datasetEmploy Keras to build autoencoder models for the CIFAR-10 datasetApply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction dataWho this book is for This course is designed for developers, data scientists, and machine learning enthusiasts who are interested in unsupervised learning. Some familiarity with Python programming along with basic knowledge of mathematical concepts including exponents, square roots, means, and medians will be beneficial.

Download or read book Hands On Unsupervised Learning Using Python written by Ankur A. Patel and published by "O'Reilly Media, Inc.". This book was released on 2019-02-21 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks

Download or read book Applied Unsupervised Learning with R written by Alok Malik and published by Packt Publishing Ltd. This book was released on 2019-03-27 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data. Key FeaturesBuild state-of-the-art algorithms that can solve your business' problemsLearn how to find hidden patterns in your dataRevise key concepts with hands-on exercises using real-world datasetsBook Description Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection. What you will learnImplement clustering methods such as k-means, agglomerative, and divisiveWrite code in R to analyze market segmentation and consumer behaviorEstimate distribution and probabilities of different outcomesImplement dimension reduction using principal component analysisApply anomaly detection methods to identify fraudDesign algorithms with R and learn how to edit or improve codeWho this book is for Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning. Although the book is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this book, you should also know basic mathematical concepts, including exponents, square roots, means, and medians.

Download or read book Hands On Unsupervised Learning with Python written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2019-02-28 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the skill-sets required to implement various approaches to Machine Learning with Python Key FeaturesExplore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and moreBuild your own neural network models using modern Python librariesPractical examples show you how to implement different machine learning and deep learning techniquesBook Description Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges. What you will learnUse cluster algorithms to identify and optimize natural groups of dataExplore advanced non-linear and hierarchical clustering in actionSoft label assignments for fuzzy c-means and Gaussian mixture modelsDetect anomalies through density estimationPerform principal component analysis using neural network modelsCreate unsupervised models using GANsWho this book is for This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.

Download or read book Hands On Unsupervised Learning Using Python written by Ankur A. Patel and published by O'Reilly Media. This book was released on 2019-02-21 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks

Download or read book Applied Supervised Learning with Python written by Benjamin Johnston and published by Packt Publishing Ltd. This book was released on 2019-04-27 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the exciting world of machine learning with the fastest growing technology in the world Key FeaturesUnderstand various machine learning concepts with real-world examplesImplement a supervised machine learning pipeline from data ingestion to validationGain insights into how you can use machine learning in everyday lifeBook Description Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own! What you will learnUnderstand the concept of supervised learning and its applicationsImplement common supervised learning algorithms using machine learning Python librariesValidate models using the k-fold techniqueBuild your models with decision trees to get results effortlesslyUse ensemble modeling techniques to improve the performance of your modelApply a variety of metrics to compare machine learning modelsWho this book is for Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.

Download or read book Introduction to Machine Learning with Python written by Andreas C. Müller and published by "O'Reilly Media, Inc.". This book was released on 2016-09-26 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Applied Text Analysis with Python written by Benjamin Bengfort and published by "O'Reilly Media, Inc.". This book was released on 2018-06-11 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity

Download or read book An Introduction to Statistical Learning written by Gareth James and published by Springer Nature. This book was released on 2023-08-01 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Download or read book Thoughtful Machine Learning with Python written by Matthew Kirk and published by "O'Reilly Media, Inc.". This book was released on 2017-01-16 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext. Featuring graphs and highlighted code examples throughout, the book features tests with Python’s Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you’re a software engineer or business analyst interested in data science, this book will help you: Reference real-world examples to test each algorithm through engaging, hands-on exercises Apply test-driven development (TDD) to write and run tests before you start coding Explore techniques for improving your machine-learning models with data extraction and feature development Watch out for the risks of machine learning, such as underfitting or overfitting data Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms

Download or read book Advanced Machine Learning with Python written by John Hearty and published by Packt Publishing Ltd. This book was released on 2016-07-28 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tutorial that tackles real-world computing problems through a rigorous and effective approach Who This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Apply your new found skills to solve real problems, through clearly-explained code for every technique and test Automate large sets of complex data and overcome time-consuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together In Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Style and approach This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.

Download or read book Hands on Supervised Learning with Python written by Gnana Lakshmi T C and published by BPB Publications. This book was released on 2021-01-06 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hands-On ML problem solving and creating solutions using Python KEY FEATURES _Introduction to Python Programming _Python for Machine Learning _Introduction to Machine Learning _Introduction to Predictive Modelling, Supervised and Unsupervised Algorithms _Linear Regression, Logistic Regression and Support Vector MachinesÊ DESCRIPTIONÊ You will learn about the fundamentals of Machine Learning and Python programming post, which you will be introduced to predictive modelling and the different methodologies in predictive modelling. You will be introduced to Supervised Learning algorithms and Unsupervised Learning algorithms and the difference between them.Ê We will focus on learning supervised machine learning algorithms covering Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees and Artificial Neural Networks. For each of these algorithms, you will work hands-on with open-source datasets and use python programming to program the machine learning algorithms. You will learn about cleaning the data and optimizing the features to get the best results out of your machine learning model. You will learn about the various parameters that determine the accuracy of your model and how you can tune your model based on the reflection of these parameters. WHAT WILL YOU LEARN _Get a clear vision of what is Machine Learning and get familiar with the foundation principles of Machine learning. _Understand the Python language-specific libraries available for Machine learning and be able to work with those libraries. _Explore the different Supervised Learning based algorithms in Machine Learning and know how to implement them when a real-time use case is presented to you. _Have hands-on with Data Exploration, Data Cleaning, Data Preprocessing and Model implementation. _Get to know the basics of Deep Learning and some interesting algorithms in this space. _Choose the right model based on your problem statement and work with EDA techniques to get good accuracy on your model WHO THIS BOOK IS FOR This book is for anyone interested in understanding Machine Learning. Beginners, Machine Learning Engineers and Data Scientists who want to get familiar with Supervised Learning algorithms will find this book helpful. TABLE OF CONTENTS Ê1. ÊIntroduction to Python Programming Ê2. Python for Machine LearningÊÊÊÊÊ Ê3.Ê Introduction to Machine LearningÊÊÊÊÊÊÊÊÊ Ê4. Supervised Learning and Unsupervised LearningÊÊÊÊÊÊÊÊÊ Ê5. Linear Regression: A Hands-on guideÊÊÊ Ê6. Logistic Regression Ð An Introduction Ê7. A sneak peek into the working of Support Vector machines(SVM)ÊÊÊÊÊÊ Ê8. Decision Trees Ê9. Random Forests Ê10. ÊTime Series models in Machine Learning Ê11.Ê Introduction to Neural Networks Ê12. ÊÊÊRecurrent Neural Networks Ê13. ÊÊÊConvolutional Neural Networks Ê14. ÊÊÊPerformance Metrics Ê15. ÊÊÊIntroduction to Design Thinking Ê16. Ê Design Thinking Case Study

Download or read book Beginning Anomaly Detection Using Python Based Deep Learning written by Sridhar Alla and published by Apress. This book was released on 2019-10-10 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: Utilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks. This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applications of the following deep learning models to anomaly detection: various types of Autoencoders, Restricted Boltzmann Machines, RNNs & LSTMs, and Temporal Convolutional Networks. The book explores unsupervised and semi-supervised anomaly detection along with the basics of time series-based anomaly detection. By the end of the book you will have a thorough understanding of the basic task of anomaly detection as well as an assortment of methods to approach anomaly detection, ranging from traditional methods to deep learning. Additionally, you are introduced to Scikit-Learn and are able to create deep learning models in Keras and PyTorch. What You Will LearnUnderstand what anomaly detection is and why it is important in today's world Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn Know the basics of deep learning in Python using Keras and PyTorch Be aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and more Apply deep learning to semi-supervised and unsupervised anomaly detection Who This Book Is For Data scientists and machine learning engineers interested in learning the basics of deep learning applications in anomaly detection

Download or read book Supervised Learning with Python written by Vaibhav Verdhan and published by Apress. This book was released on 2020-10-08 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as text and images datasets. You’ll start with an introduction to machine learning, highlighting the differences between supervised, semi-supervised and unsupervised learning. In the following chapters you’ll study regression and classification problems, mathematics behind them, algorithms like Linear Regression, Logistic Regression, Decision Tree, KNN, Naïve Bayes, and advanced algorithms like Random Forest, SVM, Gradient Boosting and Neural Networks. Python implementation is provided for all the algorithms. You’ll conclude with an end-to-end model development process including deployment and maintenance of the model. After reading Supervised Learning with Python you’ll have a broad understanding of supervised learning and its practical implementation, and be able to run the code and extend it in an innovative manner. What You'll Learn Review the fundamental building blocks and concepts of supervised learning using Python Develop supervised learning solutions for structured data as well as text and images Solve issues around overfitting, feature engineering, data cleansing, and cross-validation for building best fit models Understand the end-to-end model cycle from business problem definition to model deployment and model maintenance Avoid the common pitfalls and adhere to best practices while creating a supervised learning model using Python Who This Book Is For Data scientists or data analysts interested in best practices and standards for supervised learning, and using classification algorithms and regression techniques to develop predictive models.

Download or read book Machine Learning with Python written by Abhishek Vijayvargia and published by BPB Publications. This book was released on 2018-03-01 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing code examples in python, this book introduces the concepts of machine learning with mathematical explanations and programming fundamentals. --

Download or read book Applied Machine Learning with Python written by Andrea Giussani and published by . This book was released on 2021 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Supervised Machine Learning with Python written by Taylor Smith and published by Packt Publishing Ltd. This book was released on 2019-05-27 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Teach your machine to think for itself! Key FeaturesDelve into supervised learning and grasp how a machine learns from dataImplement popular machine learning algorithms from scratch, developing a deep understanding along the wayExplore some of the most popular scientific and mathematical libraries in the Python languageBook Description Supervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine “learns” under the hood. This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You’ll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning. By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems. What you will learnCrack how a machine learns a concept and generalize its understanding to new dataUncover the fundamental differences between parametric and non-parametric modelsImplement and grok several well-known supervised learning algorithms from scratchWork with models in domains such as ecommerce and marketingExpand your expertise and use various algorithms such as regression, decision trees, and clusteringBuild your own models capable of making predictionsDelve into the most popular approaches in deep learning such as transfer learning and neural networksWho this book is for This book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming—and some fundamental knowledge of supervised learning—are expected.