AI Mastery- Recommendation Engines Unleashed

AI Mastery: Recommendation Engines Unleashed, available at $19.99, has an average rating of 4.4, with 40 lectures, based on 5 reviews, and has 5697 subscribers.

You will learn about The fundamentals of Recommendation Engines, including collaborative filtering. Setting up the environment with Anaconda, downloading datasets, and using the Surprise library. Implementing cross-validation models for training and testing predictions. Developing functions for making movie predictions and creating a basic Book Recommender. Exploring advanced topics like content-based recommendation and feature extraction. Building an Advanced Book Recommender with hybrid models and user-specific recommendations. Developing a Movie Recommendation Engine, covering simple and content-based recommenders. Throughout the course, students will gain practical experience through hands-on projects, enhancing their skills in building effective recommendation systems. This course is ideal for individuals who are Individuals interested in mastering the basics of Recommendation Engines and collaborative filtering. or Data science enthusiasts looking to gain hands-on experience in building practical recommendation systems. or Professionals aiming to enhance their skills in data analysis and recommendation algorithm implementation. or Students and researchers seeking a comprehensive understanding of advanced recommendation techniques and content-based models. or Anyone looking to apply recommendation engine concepts to real-world projects, such as book and movie recommenders. It is particularly useful for Individuals interested in mastering the basics of Recommendation Engines and collaborative filtering. or Data science enthusiasts looking to gain hands-on experience in building practical recommendation systems. or Professionals aiming to enhance their skills in data analysis and recommendation algorithm implementation. or Students and researchers seeking a comprehensive understanding of advanced recommendation techniques and content-based models. or Anyone looking to apply recommendation engine concepts to real-world projects, such as book and movie recommenders.

Enroll now: AI Mastery: Recommendation Engines Unleashed

Summary

Title: AI Mastery: Recommendation Engines Unleashed

Price: $19.99

Average Rating: 4.4

Number of Lectures: 40

Number of Published Lectures: 40

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 40

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

The fundamentals of Recommendation Engines, including collaborative filtering.

Setting up the environment with Anaconda, downloading datasets, and using the Surprise library.

Implementing cross-validation models for training and testing predictions.

Developing functions for making movie predictions and creating a basic Book Recommender.

Exploring advanced topics like content-based recommendation and feature extraction.

Building an Advanced Book Recommender with hybrid models and user-specific recommendations.

Developing a Movie Recommendation Engine, covering simple and content-based recommenders.

Throughout the course, students will gain practical experience through hands-on projects, enhancing their skills in building effective recommendation systems.

Who Should Attend

Individuals interested in mastering the basics of Recommendation Engines and collaborative filtering.

Data science enthusiasts looking to gain hands-on experience in building practical recommendation systems.

Professionals aiming to enhance their skills in data analysis and recommendation algorithm implementation.

Students and researchers seeking a comprehensive understanding of advanced recommendation techniques and content-based models.

Anyone looking to apply recommendation engine concepts to real-world projects, such as book and movie recommenders.

Target Audiences

Individuals interested in mastering the basics of Recommendation Engines and collaborative filtering.

Data science enthusiasts looking to gain hands-on experience in building practical recommendation systems.

Professionals aiming to enhance their skills in data analysis and recommendation algorithm implementation.

Students and researchers seeking a comprehensive understanding of advanced recommendation techniques and content-based models.

Anyone looking to apply recommendation engine concepts to real-world projects, such as book and movie recommenders.

Welcome to the cutting-edge course on “AI Mastery: Recommendation Engines Unleashed”. This comprehensive program is meticulously crafted to equip participants with the knowledge and skills needed to master the intricacies of recommendation engines. Whether you are a data enthusiast, aspiring data scientist, or industry professional seeking to enhance your AI expertise, this course promises a transformative learning experience.

Course Overview:

In this journey through recommendation engines, you’ll delve into the core principles, algorithms, and practical applications that power personalized content suggestions. From understanding collaborative filtering to building sophisticated book and movie recommendation systems, each section is designed to deepen your expertise in this dynamic field.

What Sets This Course Apart:

Hands-On Projects: Immerse yourself in real-world projects, including building a Book Recommender and an Advanced Book Recommender, ensuring practical application of acquired knowledge.

Comprehensive Coverage: Cover the fundamentals, advanced techniques, and even transition seamlessly from book to movie recommendation engines.

Industry-Relevant Skills: Gain insights into the latest tools, techniques, and best practices used in the industry, ensuring your skills are up-to-date and aligned with current trends.

Section 1: Recommendation Engine – Basics

In this foundational section, participants will be introduced to the basics of recommendation engines. Starting with an insightful project overview, Lecture 2 delves into the collaborative filtering technique. Lectures 3 to 7 guide learners through setting up the Anaconda environment, downloading datasets, creating a Surprise Data frame, implementing cross-validation models, and making accurate train-test predictions. Lecture 8 concludes the section by applying these concepts to predict movie preferences.

Section 2: Project On Recommendation Engine: Book Recommender

This section initiates a practical project focused on building a Book Recommender. Lectures 9 to 23 meticulously guide learners through each stage of the project. Starting with an introduction and case study, subsequent lectures cover essential aspects like handling numerical columns, creating functions, sorting books, and developing a content-based recommender. Lecture 23 introduces techniques such as the Soup Function and Reset Index Function, crucial for extracting meaningful features.

Section 3: Project On Recommendation Engine: Advanced Book Recommender

Building upon the foundational knowledge, Section 3 introduces an advanced project in Book Recommendation. Lectures 24 to 34 cover crucial steps, including entering new book names, handling user data, implementing baselines, working with user IDs and book indices, and importing necessary libraries. The section concludes with the development of a Hybrid Model, showcasing the integration of multiple recommendation techniques for enhanced accuracy.

Section 4: Develop A Movie Recommendation Engine

This concluding section extends the learning by transitioning from books to movies. Lectures 35 to 40 guide participants through the development of a Movie Recommendation Engine. Starting with an introduction, participants will import essential libraries and progress through creating a Simple Recommender and Content-Based Recommender. The section culminates with learners equipped to develop effective recommendation systems tailored for the movie industry.

Throughout the course, participants will acquire hands-on experience, gaining the skills required to construct versatile recommendation engines applicable to diverse domains.

Course Curriculum

Chapter 1: Recommendation Engine – Basics

Lecture 1: Introduction to Project

Lecture 2: Collaborative Filtering

Lecture 3: Anaconda Setup Dataset Download

Lecture 4: Surprise Data frame

Lecture 5: Cross Validation Model

Lecture 6: Train Test Prediction

Lecture 7: Function For Prediction

Lecture 8: Movie Prediction

Chapter 2: Project On Recommendation Engine: Book Recommender

Lecture 1: Introduction to Project

Lecture 2: Case Study

Lecture 3: Numerical Cols

Lecture 4: Functions

Lecture 5: Rename Notebook

Lecture 6: Variable Name

Lecture 7: Publication Date

Lecture 8: Developing function

Lecture 9: Sort Book

Lecture 10: Content Based

Lecture 11: Feature Extraction

Lecture 12: Content Recommender

Lecture 13: Import Data

Lecture 14: Soup Function

Lecture 15: Reset Index Function

Chapter 3: Project On Recommendation Engine: Advanced Book Recommender

Lecture 1: Introduction to Project

Lecture 2: Enter a New Book Name

Lecture 3: Users Data

Lecture 4: Baseline

Lecture 5: Users ID

Lecture 6: User ID Column

Lecture 7: Book ID Index

Lecture 8: Import Pandas

Lecture 9: Hybrid

Lecture 10: Import NumPy

Lecture 11: Hybrid Model

Chapter 4: Develop A Movie Recommendation Engine

Lecture 1: Intro to Develop A Movie Recommendation Engine

Lecture 2: Importing Libraries for the Project

Lecture 3: Simple Recommender

Lecture 4: Simple Recommender Continue

Lecture 5: Content Based Recommender

Lecture 6: Content Based Recommender Continue

Instructors

EDUCBA Bridging the Gap
Learn real world skills online

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