Beyond machine and Datalyst Academy are presenting you with a unique Bootcamp.

Machine Learning & Deep Learning Bootcamp: Building Recommender System. Instructed by Dr. Stylianos Kampakis

Do you want to learn how to build a recommender system? Or are you a company that is looking to implement a recommender system, but you are not sure where to start? This course will teach you how! The course is designed in a unique format, that allows online, offline and classroom interaction, and allows the participants to learn at their own pace at home or anywhere with an internet connection.

What am I going to get from this course?

• What are recommendation engines
• 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

The course is done in partnership with Datalyst Academy: a data science bootcamp platform that combines the best approaches from online learning, offline learning, university courses, bootcamps and workshops.

The course is structured as follows:

1) Kick-off LIVE REALTIME webinar. There will be a 2-hour introductory webinar, during which the participants will be introduced to basic machine learning concepts and the course. The participants can either take the webinar from the comfort of their own home/office or come to meet other participants in our space.

2) Online platform. The participants will get access to the online content which includes both videos and curated content. They can study the content at their own pace.

3) Once the participants finish with the content, they will start working on the lab. They have 2 weeks to complete the lab.3) During this process, the participants will be mentored by the instructor, through our slack channel, and skype (if need be).

4) 2-hours final LIVE REAL-TIME webinar, which will be done in a classroom. The participants will discuss any questions they might have with the trainer directly. The participants who purchased VIP TICKETS can choose to work on their own recommender system(e.g: from your company or your personal project). They also get 2-weeks of extra mentorship.

Also, anyone who signs up for this workshop gets 20% off from the Datalyst Academy program!

Why do we introduce this new format?

Beyond Machine has done training offline over the last 2.5 years. Although we believe in an offline meeting, in reality, there still comes numerous constraints and high risk of canceling and not able to make it due to traveling incidents for both our trainers and participants. Not mention the scalability of the training. We still would like to keep an offline touch, but we also add up the new format of online. Therefore, participants can participate anywhere they want, they can also study and prepare the course materials before because we all know to go through the lab data, it takes probably around 8 hours not 3 hours, and it just makes better sense to work on it before by yourself. Of course, you can certainly come and join us on the same day in Berlin. We are a partner with Datalyst Academy which was founded by Stylianos Kampskis, our best trainer. Our Bootcamp will keep the seat and time-limited. therefore, if you want to join. Sign up now before the price goes up in 2020.

Curriculum

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

Who should attend?

• Data scientist who wants robust or learn more different applications
• Data 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 the data scientist
• Business Intelligence (BI): You are already familiar with statistics, want to understand better machine learning and deep learning

Testimonial:

Stylianos' provided well-prepared, comprehensive course materials and code and his explanations were clear and concise. The format and small group size allowed everyone to get involved and productively discuss the topics. Overall the recommender bootcamp was well-organised, highly informative and very enjoyable.

Dónal Flanagan, Machine Learning Engineer, Solvemate GmbH.

What will participants need to know or do before starting this course?

This course is ideal for the data analyst, 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 to irene@beyond-machine.com

What does the price reflect?

The price reflects two things. First, it distills 10+ years of experience in a few hours, providing only the most relevant pieces of knowledge, without all the useless 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.

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 Ph.D. 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 thedatascientist.com.

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