Name: Building Recommendation Systems with Python (TTAI2360)
Availability: InStock
Rating: 4.6 (5878 reviews)

Duration:

24.00 hours

19.5 CPD hours

Overview:

Recommendation systems are at the heart of almost every internet business today; from Facebook to Net ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques. Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.

Description:

Recommendation systems are at the heart of almost every internet business today; from Facebook to Net ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques. Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.

Course Outline:

Getting Started with Recommender Systems

Technical requirements
What is a recommender system
Types of recommender systems

Manipulating Data with the Pandas Library

Technical requirements
Setting up the environment
The Pandas library
The Pandas DataFrame
The Pandas Series

Building an IMDB Top 250 Clone with Pandas

Technical requirements
The simple recommender
The knowledge-based recommender

Building Content-Based Recommenders

Technical requirements
Exporting the clean DataFrame
Document vectors
The cosine similarity score
Plot description-based recommender
Metadata-based recommender
Suggestions for improvements

Getting Started with Data Mining Techniques

Problem statement
Similarity measures
Clustering
Dimensionality reduction
Supervised learning
Evaluation metrics

Building Collaborative Filters

Technical requirements
The framework
User-based collaborative filtering
Item-based collaborative filtering
Model-based approaches

Hybrid Recommenders

Technical requirements
Introduction
Case study and final project Building a hybrid model

, DAY ONE

Getting Started with Recommender Systems
Technical requirements
What is a recommender system
Types of recommender systems
Hands-on Activity / Lab
Manipulating Data with the Pandas Library
Technical requirements
Setting up the environment
The Pandas library
The Pandas DataFrame
The Pandas Series
Lab
Building your First Recommender with Pandas
Technical requirements
The simple recommender
The knowledge-based recommender
Hands-on Activity / Lab
Building Content-Based Recommenders
Technical requirements
Exporting the clean DataFrame
Document vectors
The cosine similarity score
Plot description-based recommender
Metadata-based recommender
Suggestions for improvements
Hands-on Activity / Lab

DAY TWO

Getting Started with Data Mining Techniques
Problem statement
Similarity measures
Clustering
Dimensionality reduction
Supervised learning
Evaluation metrics
Hands-on Activity / Lab
Building Collaborative Filters
Technical requirements
The framework
User-based collaborative filtering
Item-based collaborative filtering
Model-based approaches
Hands-on Activity / Lab
Using PineCone
Technical requirements
Introduction
Case study and project
Hands-on Activity / Lab

DAY THREE or OPTIONAL CONTENT

Deploying as a Serverless Component (OPTIONAL)
Technical requirements
Introduction
Deploy as a Serverless Service
Generative AI and Its Magic with GPT
Introduction to GPT and Generative AI
GPT in Recommendation Systems
Explore GPT's role in fine-tuning user preferences.
Lab
Ethical AI €œ Navigating the Grey Areas
Understanding Ethical Implications in AI
Grasp the moral complexities in recommendation systems.
Bias and Fairness in Recommenders
Dissect potential biases in AI-driven recommendations.
Lab
Job Aids Using Generative AI
Introduction to AI-Powered Job Aids
Understand how GPT can aid daily tasks.
Applications in Data Processing and Analysis
Learn GPT's role in data analytics enhancements.
Lab

Available Delivery Options for Building Recommendation Systems with Python (TTAI2360)

Live Instructor Led Classroom Online (Live Online)
Traditional Instructor Led Classroom training (TILT/ILT)
Delivery at your offices in United Kingdom
Private dedicated course for your staff

How many CPD hours does Building Recommendation Systems with Python (TTAI2360) provide?

The 3 day Building Recommendation Systems with Python (TTAI2360) course gives you up to 19.5 CPD hours/structured learning hours.

Who is the correct audience for Building Recommendation Systems with Python (TTAI2360)?

This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web.

Do you provide training for Building Recommendation Systems with Python (TTAI2360)?

Yes, we provide corporate training, dedicated training and closed classes for Building Recommendation Systems with Python (TTAI2360). This can take place anywhere in United Kingdom including London, Manchester, Birmingham, Edinburgh or live online.

What is the duration of Building Recommendation Systems with Python (TTAI2360)?

The Building Recommendation Systems with Python (TTAI2360) training takes place over 3 day(s), with each day lasting approximately 24.00 hours including breaks.

What other terms do people search for?

Popular related searches include: AI; Machine Learning; Applied AI; Python

Why are Nexus Human the best provider for Building Recommendation Systems with Python (TTAI2360)?

Nexus Human are recognised as one of the best training companies. Our trainers have won awards including Small Firms Best Trainer, national training partner of the year for Ireland, and global top 30 instructor awards in 2012, 2019 and 2021.

Is there a discount code for Building Recommendation Systems with Python (TTAI2360)?

Yes, the discount code PENPAL5 is currently available for Building Recommendation Systems with Python (TTAI2360) training. Only one discount code can be used per booking.

Building Recommendation Systems with Python (TTAI2360)