Donnerstag, 28. März 2019, Kreuzberg, Machine Learning Bootcamp: Building Recommender System

Von 28. März 2019 - 9:30 Bis 13:00


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“Stylianos brings great enthusiasm to his workshop – his interest in all things AI shines through.” - Tim Gordon, Chief Executive at the Liberal Democrats
"Stylianos’s bespoke workshop allows for in-depth complicated analytical concepts to be understood in a manageable and easy way. Coving the background of the constant changing world of data science and breaking down the key concepts of data science." - Dominik Byrne, Investor, Entrepreneur, Advisor
You can find more testimonials here. 

Machine Learning For Developers and Analysts- Predictive Analysis
Instructed by Dr. Stylianos Kampakis

What am I going to get from this course?

What are recommendation engines

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How does a recommendation engine work?

Data collection and Data storage

Filtering the data

Content-based filtering and Collaborative filtering

The case study in Python using the public dataset

Building a collaborative filtering model from scratch

Building Simple popularity and collaborative filtering model 

Building a recommendation engine 

Evaluation metrics for recommendation engines

Mornging Session 
Module 1: Evaluating Recommender Systems
Module 2: A Recommender Engine Framework
Module 3: Content-Based Filtering 
Module 4: Neighbourhood-Based Collaborative Filtering 
Module 5: Matrix Factorization Methods
Afternoon Session
Module 6: Introduction to Deep Learning 
Module 7: Deep Learning For Recommender Systems and scaling up


09:30 – 10:00 Registration

10:00 – 13:00 Machine Learning on Recommendation System

13:00 - 14:00 Lunch

14:00 – 19:00 Deep Learning on Recommender System

18:00 – 18:30 Questions Time

We will send out the class materials as well as required library 2 weeks before the class, please follow the instruction and get the environment ready before coming to class.

Who should attend?

Data scientist who wants robust or learn more different applications

Analyst: Companies who would like to offer re-education to their analytics team to their data science team or just upgrade yourself from analyst to data scientist. 

Developer, who wants to know more about the algorithm or even think about switching to be the data scientist

Business Intelligence (BI): You are already familiar with statistics, want to understand better machine learning and prediction

What will participants need to know or do before starting this course?
This course is ideal for the analyst, junior data scientist, and BI also developer. A bit of knowledge of Python or machine learning will help you but it is not required. Have some familiarity with basic programming concepts or languages or statistics. Therefore, experience in Python or Machine Learning is not required but will help.
If you are not sure about your level, write us. 
What does the price reflect?
The price reflects two things. First, it distills 7+ years of experience in a few hours, providing only the most relevant pieces for decision-makers, without all the jargon and the buzzwords. Secondly, it reflects the consultancy service which participants will get out of the workshop, and which is usually charged at much higher rates.
What are the problem-solving sessions about?
During the problem-solving sessions, we will solve on the blackboard any kind of problem the audience poses. In a previous workshop, for example, one of the problems was "How can we use Twitter data to predict Bitcoin prices?". The solution included the full pipeline (from data collection to data storage), to actually solving the problem and hiring the right people. Feel free to contact me with your problems before the workshop date.

If you are interested in full-day training, please book full day here. 

P.S. The seats are limited to 12 people

¹ Dr. Stylianos Kampakis is an expert data scientist (with a decade of experience), member of the Royal Statistical Society, an honorary research fellow at the UCL Centre for Blockchain Technologies and startup consultant living and working in London. A natural polymath, with degrees in Psychology, Artificial Intelligence, Statistics, Economics and a PhD in Computer Science from University College London he loves using his broad skillset to solve difficult problems. You can learn more about his work at

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