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Download Packt Building Recommendation Systems with Python

Download Packt Building Recommendation Systems with Python
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Title: Packt Building Recommendation Systems with Python
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[FreeCoursesOnline.Me] [Packt] Building Recommendation Systems with Python [FCO] 01.Get Started with Text Mining and Cleaning Data
  • 0101.The Course Overview.mp4 (39.4 MB)
  • 0102.Exploring Recommendation Engines.mp4 (70.5 MB)
  • 0103.Working with Variables You Are Taking into Consideration.mp4 (8.5 MB)
  • 0104.Setting Up Your Working Environment.mp4 (16.4 MB)
  • 0105.Understanding Text Data Source and Variables.mp4 (26.2 MB)
  • 0106.Imputation Methods for Missing Data.mp4 (15.7 MB)
02.Collaborative Filtering-Based Recommender System
  • 0201.Understanding Collaborative Filtering.mp4 (5.6 MB)
  • 0202.Exploring the Required Functions – Logic.mp4 (4.1 MB)
  • 0203.Implementation of CF Recommender System.mp4 (5.6 MB)
  • 0204.Applying the CF Algorithm to the IMDBs Dataset.mp4 (9.9 MB)
  • 0205.Evaluating the Collaborative Filtering Recommender.mp4 (9.1 MB)
03.Content and Popularity Based Recommender Systems
  • 0301.Understanding Content-Based Recommender System.mp4 (6.4 MB)
  • 0302.Implementing the Content-Based Recommender System.mp4 (18.9 MB)
  • 0303.Understanding Popularity-Based Recommender System.mp4 (10.3 MB)
  • 0304.Implementing the Popularity-Based Recommender System.mp4 (9.6 MB)
  • 0305.Evaluating Content-Based and Popularity-Based Recommender Systems.mp4 (10.3 MB)
04.Hybrid Recommender System
  • 0401.Exploring Hybrid Filtering Techniques.mp4 (9.2 MB)
  • 0402.Working with the Required Functions – Logic.mp4 (6.9 MB)
  • 0403.Algorithm Implementation for Hybrid Recommender System.mp4 (5.1 MB)
  • 0404.Implementation of the Hybrid Recommender System.mp4 (15.6 MB)
  • 0405.Evaluating the Hybrid Recommender System.mp4 (7.4 MB)
05.Flask Web Application Using PyCharm
  • 0501.Understanding the Web Framework – Flask.mp4 (8.6 MB)
  • 0502.Setting Up the Integrated Development Environment.mp4 (16.1 MB)
  • 0503.Creating a Web Application Using Flask.mp4 (77.5 MB)
  • 0504.Implementation of a Web Application Using Flask.mp4 (150.3 MB)
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Exercise Files
  • exercise_files.zip (9.1 MB)

Info

This is the code repository for Building Recommendation Systems with Python, published by Packt.

This is the code repository for Building Recommendation Systems with Python, published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish. About the Video Course. Then you will use Machine Learning techniques to create your own algorithm, which will predict and recommend accurate data.

Start building powerful and personalized, recommendation engines with Python. Building an IMDB Top 250 Clone. Feature Engineering Made Easy . Get to Know the Author

Start building powerful and personalized, recommendation engines with Python. What is this book about? First Paragraph from the Long Description. This book covers the following exciting features: The different kinds of recommender systems. Building a content based engine to recommend movies based on movie metadata. Data mining techniques used in building recommenders. Get to Know the Author. Rounak Banik Rounak Banik is a Young India Fellow and an ECE graduate from IIT Roorkee.

We will also build a simple recommender system in Python In this article we are going to introduce the reader to recommender systems. We will also build a simple recommender system in Python.

We will also build a simple recommender system in Python. In this article we are going to introduce the reader to recommender systems. The system is no where close to industry standards and is only meant as an introduction to recommender systems. We assume that the reader has prior experience with scientific packages such as pandas and numpy. What is a recommender system? A recommender system is a simple algorithm whose aim is to provide the most relevant information to a user by discovering patterns in a dataset.

PacktPublishing, hon. hon/Video . /ui/package. Fetching contributors.

mrec Recommender Systems library - Library developed by Mendeley that has a few important methods such as SLIM . It is an on-going open source project. It is integrated with other famous numerical/machine learning libraries for python(numpy, scikit, et.

mrec Recommender Systems library - Library developed by Mendeley that has a few important methods such as SLIM or a learning-to-rank approach to matrix factorization. lightfm - A Python implementation of a hybrid e recommender system. logistic-mf - A basic Python implementation of logistic matrix factorization.

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

To build a system that can automatically recommend items to users based on the preferences of other users, the first . LightFM: a hybrid recommendation algorithm in Python. Python-recsys: a Python library for implementing a recommender system.

To build a system that can automatically recommend items to users based on the preferences of other users, the first step is to find similar users or items.


By: Eric Rodríguez
Released: 30 May 2019 (New Release!)
Torrent Contains: 32 Files, 7 Folders
Course Source: https://www.packtpub.com/big-data-and-business-intelligence/building-recommendation-systems-python-video
Build real-world recommendation systems using collaborative, content-based, and hybrid filtering techniques in Python
Video Details
ISBN 9781788991704
Course Length 1 hour 35 minutes
Table of Contents
• Get Started with Text Mining and Cleaning Data
• Collaborative Filtering-Based Recommender System
• Content and Popularity Based Recommender Systems
• Hybrid Recommender System
• Flask Web Application Using PyCharm
Learn
• Build your own recommendation engine with Python to analyze data
• Use effective text-mining tools to get the best raw data
• Master collaborative filtering techniques based on user profiles and the item they want
• Content-based filtering techniques that use user data such as comments and ratings
• Hybrid filtering technique which combines both collaborative and content-based filtering
• Utilize Pandas and sci-kit-learn easy-to-use data structures for data analysis
About
Recommendation Engines have become an integral part of any application. For accurate recommendations, you require user information. The more data you feed to your engine, the more output it can generate – for example, a movie recommendation based on its rating, a YouTube video recommendation to a viewer, or recommending a product to a shopper online.
In this practical course, you will be building three powerful real-world recommendation engines using three different filtering techniques. You'll start by creating usable data from your data source and implementing the best data filtering techniques for recommendations. Then you will use Machine Learning techniques to create your own algorithm, which will predict and recommend accurate data.
By the end of the course, you'll be able to build effective online recommendation engines with Machine Learning and Python – on your own.
The code bundle for this video course is available at - https://github.com/PacktPublishing/Building-Recommendation-Systems-with-Python
Style and Approach
This course is a step-by-step guide to building your own recommendation engine with Python. It will help you gain all the training and skills you need to make suggestions as to data that a website user might be interested in, by using various data filtering techniques.
Features:
• Understand how to work with real data using a recommendation in Python
• Graphical representation of categories or classes to visualize your data
• Comparison of different recommender systems and learning to help you choose the right one
Author
Eric Rodríguez
Eric Rodríguez is a mechatronics engineer with an interest in the areas of machine learning and robotics. His passion for programming began around 5 years ago when he started learning how to build web applications. He moved on to develop Android applications and finally completed his Master's degree in Computer Science. He has also started using C# in Xamarin to develop mobile applications. Eric has years of practical experience in the software development industry as a software engineer.
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Packt Building Recommendation Systems with Python