Feature engineering made easy: identify unique features from your dataset in order to build powerful machine learning systems

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.