Hands-On Recommendation Systems with Python : Start Building Powerful and Personalized, Recommendation Engines with Python

Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Objectives Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content based engine to recommend movies based on movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative fltering About Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. Cover Title Page Copyright and Credits Dedication Packt Upsell Contributors Table of Contents Preface Chapter 1: Getting Started with Recommender Systems Technical requirements What is a recommender system? The prediction problem The ranking problem Types of recommender systems Collaborative filtering User-based filtering Item-based filtering Shortcomings Content-based systems Knowledge-based recommenders Hybrid recommenders Summary Chapter 2: Manipulating Data with the Pandas Library Technical requirements Setting up the environment The Pandas library. The Pandas DataFrameThe Pandas Series Summary Chapter 3: Building an IMDB Top 250 Clone with Pandas Technical requirements The simple recommender The metric The prerequisties Calculating the score Sorting and output The knowledge-based recommender Genres The build_chart function Summary Chapter 4: Building Content-Based Recommenders Technical requirements Exporting the clean DataFrame Document vectors CountVectorizer TF-IDFVectorizer The cosine similarity score Plot description-based recommender Preparing the data Creating the TF-IDF matrix. Computing the cosine similarity scoreBuilding the recommender function Metadata-based recommender Preparing the data The keywords and credits datasets Wrangling keywords, cast, and crew Creating the metadata soup Generating the recommendations Suggestions for improvements Summary Chapter 5: Getting Started with Data Mining Techniques Problem statement Similarity measures Euclidean distance Pearson correlation Cosine similarity Clustering k-means clustering Choosing k Other clustering algorithms Dimensionality reduction Principal component analysis. Other dimensionality reduction techniquesLinear-discriminant analysis Singular value decomposition Supervised learning k-nearest neighbors Classification Regression Support vector machines Decision trees Ensembling Bagging and random forests Boosting Evaluation metrics Accuracy Root mean square error Binary classification metrics Precision Recall F1 score Summary Chapter 6: Building Collaborative Filters Technical requirements The framework The MovieLens dataset Downloading the dataset Exploring the data Training and test data Evaluation. User-based collaborative filteringMean Weighted mean User demographics Item-based collaborative filtering Model-based approaches Clustering Supervised learning and dimensionality reduction Singular-value decomposition Summary Chapter 7: Hybrid Recommenders Technical requirements Introduction Case study -- Building a hybrid model Summary Other Books You May Enjoy Index. A perfect guide to speed up the predicting power of machine learning algorithms About This Book Design, discover, and create dynamic, efficient features for your machine learning application Understand your data in-depth and derive astonishing data insights with the help of this Guide Grasp powerful feature-engineering techniques and build machine learning systems Who This Book Is For If you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of the machine learning concepts and Python scripting would be enough to get started with this book. What You Will Learn Identify and leverage different feature types Clean features in data to improve predictive power Understand why and how to perform feature selection, and model error analysis Leverage domain knowledge to construct new features Deliver features based on mathematical insights Use machine-learning algorithms to construct features Master feature engineering and optimization Harness feature engineering for real world applications through a structured case study In Detail Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your data--often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization. Style and approach This step-by-step guide with use cases, examples, and illustrations will help you master the concepts of feature engineering. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world. Downloading the example code for this book .. With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Book Description Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory--you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. What you will learn Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content based engine to recommend movies based on movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative fltering Who this book is for If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you .. Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn Understand the Statistical and Machine Learning fundamentals necessary to build models Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages Analyze the results and tune the model appropriately to your own predictive goals Understand the concepts of required statistics for Machine Learning Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models. Downloading the example code for this book. You can download the example code files for al.. BWith Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web/b h4Key Features/h4 ulliBuild industry-standard recommender systems /li liOnly familiarity with Python is required /li liNo need to wade through complicated machine learning theory to use this book /li /ul h4Book Description/h4 Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory- you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. h4What you will learn/h4 ulliGet to grips with the different kinds of recommender systems /li liMaster data-wrangling techniques using the pandas library /li liBuilding an IMDB Top 250 Clone /li liBuild a content based engine to recommend movies based on movie metadata /li liEmploy data-mining techniques used in building recommenders /li liBuild industry-standard collaborative filters using powerful algorithms /li liBuilding Hybrid Recommenders that incorporate content based and collaborative fltering /li /ul h4Who this book is for/h4 If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory With Hands-on Recommendation Systems With Python, Learn The Tools And Techniques Required In Building Various Kinds Of Powerful Recommendation Systems (collaborative, Knowledge And Content Based) And Deploying Them To The Web Key Features Build Industry-standard Recommender Systems Only Familiarity With Python Is Required No Need To Wade Through Complicated Machine Learning Theory To Use This Book Book Description Recommendation Systems Are At The Heart Of Almost Every Internet Business Today; From Facebook To Netflix To Amazon. Providing Good Recommendations, Whether It's Friends, Movies, Or Groceries, Goes A Long Way In Defining User Experience And Enticing Your Customers To Use Your Platform. This Book Shows You How To Do Just That. You Will Learn About The Different Kinds Of Recommenders Used In The Industry And See How To Build Them From Scratch Using Python. No Need To Wade Through Tons Of Machine Learning Theory--you'll Get Started With Building And Learning About Recommenders As Quickly As Possible.. In This Book, You Will Build An Imdb Top 250 Clone, A Content-based Engine That Works On Movie Metadata. You'll Use Collaborative Filters To Make Use Of Customer Behavior Data, And A Hybrid Recommender That Incorporates Content Based And Collaborative Filtering Techniques With This Book, All You Need To Get Started With Building Recommendation Systems Is A Familiarity With Python, And By The Time You're Fnished, You Will Have A Great Grasp Of How Recommenders Work And Be In A Strong Position To Apply The Techniques That You Will Learn To Your Own Problem Domains. What You Will Learn Get To Grips With The Different Kinds Of Recommender Systems Master Data-wrangling Techniques Using The Pandas Library Building An Imdb Top 250 Clone Build A Content Based Engine To Recommend Movies Based On Movie Metadata Employ Data-mining Techniques Used In Building Recommenders Build Industry-standard Collaborative Filters Using Powerful Algorithms Building Hybrid Recommenders That Incorporate Content Based And Collaborative Fltering Who This Book Is For If You Are A Python Developer And Want To Develop Applications For Social Networking, News Personalization Or Smart Advertising, This Is The Book For You. Basic Knowledge Of Machine Learning Techniques Will Be Helpful, But Not Mandatory. A Perfect Guide To Speed Up The Predicting Power Of Machine Learning Algorithms Key Features Design, Discover, And Create Dynamic, Efficient Features For Your Machine Learning Application Understand Your Data In-depth And Derive Astonishing Data Insights With The Help Of This Guide Grasp Powerful Feature-engineering Techniques And Build Machine Learning Systems Book Description Feature Engineering Is The Most Important Step In Creating Powerful Machine Learning Systems. This Book Will Take You Through The Entire Feature-engineering Journey To Make Your Machine Learning Much More Systematic And Effective. You Will Start With Understanding Your Data--often The Success Of Your Ml Models Depends On How You Leverage Different Feature Types, Such As Continuous, Categorical, And More, You Will Learn When To Include A Feature, When To Omit It, And Why, All By Understanding Error Analysis And The Acceptability Of Your Models. You Will Learn To Convert A Problem Statement Into Useful New Features. You Will Learn To Deliver Features Driven By Business Needs As Well As Mathematical Insights. You'll Also Learn How To Use Machine Learning On Your Machines, Automatically Learning Amazing Features For Your Data. By The End Of The Book, You Will Become Proficient In Feature Selection, Feature Learning, And Feature Optimization. What You Will Learn Identify And Leverage Different Feature Types Clean Features In Data To Improve Predictive Power Understand Why And How To Perform Feature Selection, And Model Error Analysis Leverage Domain Knowledge To Construct New Features Deliver Features Based On Mathematical Insights Use Machine-learning Algorithms To Construct Features Master Feature Engineering And Optimization Harness Feature Engineering For Real World Applications Through A Structured Case Study Who This Book Is For If You Are A Data Science Professional Or A Machine Learning Engineer Looking To Strengthen Your Predictive Analytics Model, Then This Book Is A Perfect Guide For You. Some Basic Understanding Of The Machine Learning Concepts And Python Scripting Would Be Enough To Get Started With This Book. With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Book Description Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get... COM004000 - COMPUTERS / Intelligence (AI) and Semantics,COM042000 - COMPUTERS / Natural Language Processing,COM018000 - COMPUTERS / Data Processing Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies or groceries, goes a long way in defining user experience and enticing your customers to use and buy from your platform. This book teaches you to do just that.;Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Recommender Systems; Technical requirements; What is a recommender system?; The prediction problem; The ranking problem; Types of recommender systems; Collaborative filtering; User-based filtering; Item-based filtering; Shortcomings; Content-based systems; Knowledge-based recommenders; Hybrid recommenders; Summary; Chapter 2: Manipulating Data with the Pandas Library; Technical requirements; Setting up the environment; The Pandas library.