Pricing is per delegate, giving you huge savings over the cost of individual courses.
4.6 out of 5 rating Last updated 21/11/2024 English
GTR = Guaranteed to Run
3 Days
18 CPD hours
This skills-focused ccombines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern "on-the-job" modern applied datascience, AI and machine learning experience into every classroom and hands-on project.Working in a hands-on lab environment led by our expert instructor, attendees will-Understand the different kinds of recommender systems-Master data-wrangling techniques using the pandas library-Building an IMDB Top 250 Clone-Build a content-based engine to recommend movies based on real movie metadata-Employ data-mining techniques used in building recommenders-Build industry-standard collaborative filters using powerful algorithms-Building Hybrid Recommenders that incorporate content based and collaborative filtering
Recommendation systems are at the heart of almost every internet business today; from Facebook to Netï¬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 theoryyou 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.This skills-focused ccombines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern "on-the-job" modern applied datascience, AI and machine learning experience into every classroom and hands-on project.Working in a hands-on lab environment led by our expert instructor, attendees will:-Understand the different kinds of recommender systems-Master data-wrangling techniques using the pandas library-Building an IMDB Top 250 Clone-Build a content-based engine to recommend movies based on real movie metadata-Employ data-mining techniques used in building recommenders-Build industry-standard collaborative filters using powerful algorithms-Building Hybrid Recommenders that incorporate content based and collaborative filtering
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.Attending students should have the following incoming skills:-Basic to Intermediate IT Skills.-Basic Python syntax skills are recommended. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them.-Good foundational mathematics or logic skills-Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su
Nexus Humans Applied AI: Building Recommendation Systems with Python (TTAI2360) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward.
This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the ITS Data Analytics course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.The 3 day. Applied AI: Building Recommendation Systems with Python (TTAI2360) training course give you up to 18 CPD hours/structured learning hours. If you need a letter or certificate in a particular format for your association, organisation or professional body please just ask.
The Applied AI: Building Recommendation Systems with Python (TTAI2360) prepares you for the Yes official exam. You can take this exam at any exam center across UK including, England, Scotland, Cymru (Wales) or Northern Ireland or live online where ever you are. Exams vary in duration and if required you can request with the provider for any accommodations appropriate for you.
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.
Yes we provide corporate training, dedicated training and closed classes for the Applied AI: Building Recommendation Systems with Python (TTAI2360). This can take place anywhere in UK including, England, Scotland, Cymru (Wales) or Northern Ireland or live online allowing you to have your teams from across UK or further afield to attend a single training event saving travel and delivery expenses.
The Applied AI: Building Recommendation Systems with Python (TTAI2360) training takes place over 3 day(s), with each day lasting approximately 8 hours including small and lunch breaks to ensure that the delegates get the most out of the day.
Popular related searched include AI; Machine Learning; Applied AI; Python.
Yes, the discount code PENPAL5 is currently available for the Applied AI: Building Recommendation Systems with Python (TTAI2360) training. Other discount codes may also be available but only one discount code or special offer can be used for each booking. This discount code is available for companies and individuals.
When you organise training, we understand that there is a risk that some people may fall ill, become unavailable. To mitigate the risk we include training insurance for each delegate enrolled on our public schedule, they are welcome to sit on the same Public class within 6 months at no charge, if the case arises.
Great course yesterday and can’t give enough amazing feedback for John. I’ve never had a teacher hold attention of a class from start to finish, but yesterday he did this.
Vistra
Alan delivered the training in an easy to understand and pleasant manner which was broken down into manageable chunks with an appropriate number of breaks to give folk time away from their laptops and screens. It is never easy to do 2 days of training on a fairly in-depth subject involving Software even in a classroom environment and remaining focused and engaged so doing it remotely adds another level of complexity, but I have to say that personally this was one of the best Training Events that I have attended for some years. I have gained so much knowledge of MS Project and it will certainly make my job easier going forward.
A large part of what I gained has to be credited to Alan’s manner and delivery and I should be grateful if you could pass on my thanks to him.
Also, the support, together with joining instructions & training literature etc from Nexus Human has been excellent – thank you for that. I intend feeding that back to our People and ICT Teams too
December 2023
Kerry, Ireland
ITC Group
Dublin
Nexus Human Classroom Student
Excellent interaction and answered all of my questions. Looking forward to being able to apply some of the tips and features learned today which hopefully should have real impact on some of the more time consuming tasks currently encountered.
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