Become an Expert in Building Intelligent Recommender Systems

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Building Intelligent Recommender Systems Course Overview


Building Intelligent Recommender Systems is a course designed to help students gain the fundamentals of creating and managing systems that are able to recommend relevant options for users. It involves studying the different methods of recommendation, exploring data collection and analysis, developing machine learning models, and building web applications for users.
The course is intended for students who wish to learn about creating and managing recommender systems, such as software engineers, data scientists, data engineers, and product managers. It covers a wide range of topics, from recommendation algorithms and data processing strategies to web development and deployment strategies. In addition, it provides examples and tools such as movie, music, and book recommendation services to reinforce these concepts.
The course is divided into several modules, which include data mining, probabilistic modeling, natural language processing, and machine learning algorithms. By the end of the course, students will be able to create and manage their own personalized recommenders. The course also provides guidelines on how to evaluate and improve the accuracy and performance of recommender systems.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

Building Intelligent Recommender Systems
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The 1-on-1 Advantage

Get 1on-1 session with our expert trainers at a date & time of your convenience.
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Flexible Dates

Start your session at a date of your choice-weekend & evening slots included, and reschedule if necessary.
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4-Hour Sessions

Training never been so convenient- attend training sessions 4-hour long for easy learning.
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Destination Training

Attend trainings at some of the most loved cities such as Dubai, London, Delhi(India), Goa, Singapore, New York and Sydney.
Live Online Training (Duration : 8 Hours) Fee On Request
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

4 Hours
8 Hours
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
Date On Request
Building Intelligent Recommender Systems
Course Prerequisites

Theoretical:
1. Basic knowledge in Algorithms and Data Structures
2. Statistical Modelling
3. Basics of Machine Learning
4. Mathematics – Linear Algebra and Multivariate Calculus
Technical Skills:
1. Programming experience in Python and related libraries (e.g. scikit-learn and Tensorflow)
2. Knowledge of Services in the Cloud, such as Amazon Web Services
3. Understanding of Data Structures and Database Systems
4. Familiarity with Natural Language Processing techniques

Target Audience


The target audience for a Building Intelligent Recommender Systems training would include enterprise engineers, data scientists, software developers, and other professionals with an interest in building machine learning models and leveraging them to build intelligent recommendation systems
The attendees should have a basic understanding of machine learning principles and be able to work with Python and its data science libraries
The training should touch upon core aspects of recommendation systems, such as user profiling, collaborative filtering, hybrid or content-based recommendation, and natural language processing
In addition, this training should focus on a hands-on, workshop-style approach, so whether you're a novice or experienced in the field, you'll come away better prepared to build, evaluate, and deploy your own recommender system

Learning Objectives of Building Intelligent Recommender Systems


1. Understand how to build, implement and test end-to-end recommendation systems.
2. Learn about the different components and architectures for building intelligent recommender systems.
3. Develop an understanding of how Collaborative Filtering algorithms work and how to apply them for large-scale personalization.
4. Interpret the various evaluation metrics and metrics used to evaluate the accuracy of the recommender engine.
5. Learn how to develop Hybrid Recommender Systems, combining content- and collaborative filtering approaches.
6. Understand how to integrate post-deployment and contextual information into the personalization process.
7. Be familiar with scalable approaches to training large data volumes, including methods such as item-based and pairwise learning.
8. Explore the use of neural-networks in recommenders such as AutoML/DL models.

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FAQ's

Yes, fee excludes local taxes.
Yes, we do.
Schedule for Group Training is decided by Koenig. Schedule for 1-on-1 is decided by you.
In 1 on 1 Public you can select your own schedule, other students can be merged. Choose 1-on-1 if published schedule doesn't meet your requirement. If you want a private session, opt for 1-on-1 Private.
1-on-1 Public - Select your start date. Other students can be merged. 1-on-1 Private - Select your start date. You will be the only student in the class.
Yes, course requiring practical include hands-on labs.
You can buy online from the page by clicking on "Buy Now". You can view alternate payment method on payment options page.
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You can request a refund if you do not wish to enroll in the course.
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Yes, we also offer weekend classes.
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It is recommended but not mandatory. Being acquainted with the basic course material will enable you and the trainer to move at a desired pace during classes. You can access courseware for most vendors.
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Buy-Now. Pay-Later option is available using credit card in USA and India only.
You will receive the digital certificate post training completion via learning enhancement tool after registration.
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You can pay through debit/credit card or bank wire transfer.
Dubai, London, Sydney, Singapore, New York, Delhi, Goa, Bangalore, Chennai and Gurugram.
Yes you can request your customer experience manager for the same.
Yes of course. 100% refund if training not upto your satisfaction.

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