Course Description Understand How Online Recommendations Work by Building a Movie App In this ’Recommendation Systems in Python’ online course, you’ll learn about key concepts such as content-based filtering, collaborative filtering, neighborhood models, matrix factorization, and more! By the time you’ve finished the training, you’ll be able to build a movie recommendation system in Python by mastering both theory and practice. Supplemental Material included! Recommendation Engines perform a variety of tasks, but the most important one is to find products that are most relevant to the user. Follow along with this intensive Recommendation Systems in Python training course to get a firm grasp on this essential Machine Learning component. Requirements: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Highlights: Learn about Movielens – a famous dataset with movie ratings Use Pandas to read and play around with the data Learn how to use Scipy and Numpy Introduction to Latent Factor Methods Introduction to Memory-based Approaches Design andamp; implement a Recommendation System in Python Target Audience: Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning Engineers who want to understand or learn machine learning and apply it to problems they are solving Product managers who want to have intelligent conversations with data scientists and engineers about machine learning Tech executives and investors who are interested in big data, machine learning or natural language processing MBA graduates or business professionals who are looking to move to a heavily quantitative role Example Video Course Outline Chapter 01: Would You Recommend to a Friend? Lesson 01: Introduction: You, This Course andamp; Us! Lesson 02: What do Amazon and Netflix have in common? Lesson 03: Recommendation Engines: a look inside Lesson 04: What are you made of? Content-Based Filtering Lesson 05: With a little help from friends: Collaborative Filtering Lesson 06: A Model for Collaborative Filtering Lesson 07: Top Picks for You! Recommendations with Neighborhood Models Lesson 08: Discover the Underlying Truth: Latent Factor Collaborative Filtering Lesson 09: Latent Factor Collaborative Filtering continued Lesson 10: Gray Sheep andamp; Shillings: Challenges with Collaborative Filtering Lesson 11: The Apriori Algorithm for Association Rules Chapter 02: Recommendation Systems in Python Lesson 01: Installing Python : Anaconda andamp; PIP Lesson 02: Back to Basics: Numpy in Python Lesson 03: Back to Basics: Numpy andamp; Scipy in Python Lesson 04: Movielens andamp; Pandas Lesson 05: Code Along: What’s my favorite movie? – Data Analysis with Pandas Lesson 06: Code Along: Movie Recommendation with Nearest Neighbor CF Lesson 07: Code Along: Top Movie Picks (Nearest Neighbor CF) Lesson 08: Code Along: Movie Recommendations with Matrix Factorization Lesson 09: Code Along: Association Rules with the Apriori Algorithm PACKAGE INCLUDES: Length of Subscription: 12 Months Online On-Demand Access Running Time: 4 hrs 30 min Platform: Windows andamp; MAC OS Level: Beginner to Advanced Project Files: Included Learn anytime, anywhere, at home or on the go. Stream your training via the internet, or download to your computer and supported mobile device, including iPad, iPhone, iPod Touch and most Android devices. Need to train your Team? Contact Us for Discounts on Multiple Subscription Purchases.
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