Mastering Recommender Systems: Path from Novice to Expert
Practical Guide to Building Recommender Systems: Turning Theory into Real-World Applications using Recommender Systems
What you will learn
• Learn the about basics of recommender systems
• Learn the basics impact of recommender systems with integrated artificial intelligence
• Learn about the major challenges and applications of recommender systems
• Learn the basic taxonomy of recommender systems
• Learn the impact of overfitting, underfitting, bias and variance
• Learn the fundamental concepts of content based filtering and collaborative filtering
• Learn the hands-on development of recommender system using machine learning topologies with python
• Learn building the recommender system for various recommender system applications such as Spotify song recommending systems using machine learning and python
• Hands on experience to build content-based recommender systems with machine learning and python
• Hands on experience to build item-based recommender systems using machine learning techniques and python
• Learn to model k-nearest neighbors-based recommender engine for various types of applications of recommender systems in python
• Learn the about deep learning of recommender systems
• Learn the about benefits and challenges of deep learning in recommender systems
• Learn about the mechanism of generic deep learning-based approaches for recommender system
• Learn the basic neural network models for recommendations
• Learn the theoretical aspects of neural collaborative filtering and variational auto encoders for collaborative filtering
• Learn the hands-on practice for the implementation of deep learning-based recommender system
• Learn about the implementation of two-tower model and its implementation for development of recommender systems
• Learn the implementation of TensorFlow recommenders for the development of recommender systems
• And much more…
Why take this course?
Course description
Have you ever wondered how YouTube tailors your feed to your favorite content, or why Netflix recommends your favorite TV shows? Are you eager to create your own customized recommender system? If the answer is yes, then this is the course you've been searching for!
Unlike other courses, our comprehensive package is designed for beginners, offering a step-by-step journey into the world of recommender systems. You'll learn the fundamentals, applications, and how to build them from the ground up using Python, machine learning, and deep learning.
Every module is filled with engaging content, combining essential theoretical concepts with hands-on practical exercises. At the end of each module, you'll put your knowledge to the test with quizzes, and we'll provide solutions in the next video.
Starting with the theoretical underpinnings of recommender systems, we'll equip you with a solid understanding of the key taxonomies that form the building blocks of these systems.
Our course will take you from basics to advanced techniques in developing recommender systems using Python, machine learning, and deep learning. You'll gain proficiency in Python, from the fundamentals to advanced levels, ensuring you can implement machine learning and deep learning concepts effectively.
With a practical approach, you'll learn to evaluate your recommender systems using real-world data, including user ratings, preferences, music genres, movie categories, and release years. You'll also gain hands-on experience building content-based and collaborative filtering techniques for recommender systems.
This course isn't just about theory; it's packed with projects to provide valuable hands-on experience.
Machine learning is in high demand, with machine learning engineers earning an average salary of over $110,000 in the United States. It's a rewarding career that allows you to tackle some of the world's most intriguing challenges.
Our course caters to beginners with some programming experience or even those entirely new to data analysis, machine learning, and RNNs.
Comparable courses on Recommender Systems using Machine Learning and Deep Learning can cost thousands of dollars. However, you can access all this knowledge at a fraction of the cost in just one course. With over 6 hours of HD video lectures, divided into numerous videos and detailed code notebooks, this is one of Udemy's most comprehensive courses on Recommender Systems using Machine Learning and Deep Learning.
Why should you enroll in this course?
This course is meticulously designed to not only help you understand the role and impact of recommender systems in real-world applications but also provide a unique hands-on experience in developing complete recommender system engines tailored to your dataset through various projects. Our learning-by-doing approach will master the concepts and methodology related to Python.
This course is:
Easy to understand
Expressive and self-explanatory
To the point
Practical, with live coding
A complete package featuring three in-depth projects that cover the entire course content
Thorough, covering the most advanced and recently discovered machine learning models by renowned data scientists and AI practitioners
Teaching is our passion:
We focus on creating online tutorials that encourage learning by doing. Our aim is to offer more than a superficial look at the practical approach to building recommender systems using machine learning, with a particular focus on content-based filtering and collaborative filtering. Our course includes two projects in the final module, allowing you to experiment and gain practical insights into implementing machine learning with data analysis using real-world datasets from movies and Spotify songs. We've put in extra effort to ensure you grasp the concepts clearly, with a strong foundation in the basics before moving on to more complex topics. Our course materials include high-quality video content, course notes, meaningful course materials, handouts, and evaluation exercises. Plus, you can always reach out to our friendly team if you have any questions.
Course Content:
We'll teach you how to program with Python and harness machine learning concepts to build recommender systems. Here's just a glimpse of what you'll learn:
Course Overview
Motivation for Recommender Systems
Recommender Systems Process
Goals of Recommender Systems
Generations of Recommender Systems
Nexus of Recommender Systems with Artificial Intelligence
Real World Challenges of Recommender Systems
Applications of Recommender Systems
Basics of Recommender Systems
Taxonomy of Recommender Systems
Item-context Matrix
User-Rating Matrix
Inferring Preferences
Quality of Recommender Systems
Online and Offline Evaluation Techniques
Dataset Partitioning
Overfitting
Error Matrix
Content-based Filtering
Collaborative Filtering
User-based and Item-based Collaborative Filtering
Recommender Systems with Machine Learning
Machine Learning in Recommender Systems
Benefits of Machine Learning in Recommender Systems
Design Approaches for Recommender Systems using Machine Learning
Guidelines for Machine Learning-based Recommender Systems
Hands-on Practical Approach for Content-Based Filtering using Machine Learning
Hands-on Practical Approach for Item-based Collaborative Filtering using Machine Learning
Project 1: Songs Recommendation System for a Music Application using Machine Learning
Project 2: Movie Recommendation System using K-nearest Neighbors Algorithm
Deep Learning for Recommender Systems
Overview of Deep Learning in Recommendation Systems
Benefits and Challenges of Deep Learning in Recommender Systems
Deep Learning for Recommendation Inference
A Generic Deep Learning-based Recommendation Approach
Neutral Collaborative Filtering
Project: Amazon Product Recommendation System
Packages Installation
Data Analysis for Products Recommendation
Data Preparation
Model Development using Two-tower Approach
Implementing TensorFlow Recommenders
Fitting and Evaluation of Recommender System
Validation of Recommender System
Testing a Recommender Model
Making Predictions using Recommender Systems
Unlock the potential of recommender systems and elevate your skills in applied machine learning and deep learning. Whether you want to build customized recommender systems, implement machine learning and deep learning algorithms, or you're passionate about content-based and collaborative filtering-based recommenders, this course is tailored to you.
Join us today and embark on a journey towards becoming a recommender systems expert!
After successfully completing this course, you'll be able to:
Understand and implement machine learning models for building real-world recommendation systems.
Understand and implement deep learning models for building real-world recommendation systems.
Evaluate machine learning and deep learning models effectively.
Don't miss this opportunity to take your career to the next level. Enroll now!"
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