Adevinta Avenues Competencies

At the end of this course you will have worked on the following competencies: Life-Longer Learner, Curious Analyst, Metrics creation, Measurement/Evaluation

Adevinta Avenues

Who is the course for ?

  • People who have completed ML for Business I

  • All Business profiles interested in learning deep concepts of ML

  • People who want experience with real ML cases

  • Content only available for Adevinta employees

Course Structure

  • 7 core modules

    Go at your own pace

  • 5 hours of tailored content

    Advanced level of ML for Business, focusing on what's available in Adevinta

  • Sharing ideas

    Recorded videos, quizzes, and ML brainstorming gatherings

Course curriculum

  • 1

    Welcome to Business II!

    • Welcome!
  • 2

    ML and the product development cycle

    • 01- Introduction by Raquel Sainz

    • 02- Problem definition - Ciera Crowell

    • MLB06 - Test your learning- Problem Definition

    • 03- Hypothesis - Ciera Crowell

    • MLB06 - Test your learning- Hypothesis

    • 4.1 - Assessing when ML is or not - Raquel Sainz

    • 4.2 Assessing when ML is or not Q&A - Raquel Sainz & Manuel Sanchez

    • 5.1 KPI Intro - Raquel Sainz

    • 5.2 KPI - Online and offline metrics - Manuel Sanchez

    • MLB06 - Test your learning- KPI

    • 6.1 - Implementation, Data - Manuel Sanchez

    • MLB06 - Test your learning- Implementation, Data

    • 6.2 - Implementation, Baseline model- Manuel Sanchez

    • MLB06 - Test your learning- Implementation Baseline model

    • 6.3.1 - A/B Testing - Maria Jose Pelaez

    • 6.3.2 - A/B Testing - Maria Jose Pelaez

    • 6.4- Implementation, Improving the model - Manuel Sanchez

    • MLB06 - Test your learning- Implementation improving the model

    • 07- Roles and responsibilities & Importance of coordination - Manuel Sanchez

    • MLB06 - Test your learning- Roles & Responsabilities

    • 08- Ethics - Raquel Sainz

    • MLB06 - Test your learning- Ethics
  • 3

    The role of data

    • The importance of Data

    • Data Types

    • MLB07.2 -Test your learning - Data Types

    • Data Storage

    • MLB07.3 -Test your learning - Data Storage

    • Feature Engineering for Data Quality

    • MLB07.4 -Test your learning - Feature Engineering

    • Obtaining Data

    • MLB07.5 -Test your learning - Obtaining Data

    • Pitfalls in Data
  • 4

    Data in Adevinta (Intro)

    • 00- Introduction to Data in Adevinta

    • 1.1- What Data means in Adevinta

    • 1.2 - Sharing Data in Adevinta , Data Highway

    • 1.3 - Metrics

    • 1.4 - Recap

    • MLT-8.1- Test your learning

    • 2.1 - Introduction to datasets, producers, consumers and lineage

    • 2.2 - Principles of managing datasets

    • 2.3 -Tagging Plans

    • 2.4 -Schemas

    • 2.5-Describing datasets

    • 2.6 -Recap

    • MLT-08.2- Test your learning
  • 5

    Data in Adevinta (Advanced)

    • 3.3 -The harm of bad data

    • 3.4 -Why does low quality data happen?

    • 3.5 - Data quality lifecycle

    • MLB08 - Test your learning- Lesson 3

    • 4.1 - Tools to colect data

    • 4.2 - Tools for data exploration

    • 4.3 - Recap

    • 4.4 - Schema validation

    • 4.5 - Anomaly detection and data quality check

    • 4.6 - Alerting and reporting

    • 4.7 - Recap

    • MLB08 - Test your learning- Lesson 4
  • 6

    Barriers for scaling ML in Adevinta

    • 01- Introduction- What does scaling mean

    • 02- Status of ML in Adevinta

    • 03- Lack of knowledge and skills

    • 04-Machine learning and software development

    • 05- Inmaturity of Machine learning tooling

    • 05.1 - Regulation and social responsability

    • 06- Data as an asset

    • 07- Problems of centralized data approach

    • 08- Distributed data approach

    • 09- Importance of data culture
  • 7

    Deep Learning

    • 01- Introduction to Deep Learning

    • MLB10.1 -Test your learning

    • 02- Components of Neural Networks

    • MLB10.2 -Test your learning

    • 03 - Types of Neural Networks

    • MLB10.3 -Test your learning

    • 04 -Example in depth - CNN - Part I

    • MLB10.4 -Test your learning

    • 05 -Example in depth - CNN - Part II
  • 8

    ML techniques and use cases

    • ML11- 1 Word Embeddings

    • MLB11.1 -Test your learning

    • ML11- 2 Text classification

    • MLB11.2 -Test your learning

    • ML11- 3 Text Regression

    • MLB11.3 -Test your learning

    • ML11-4 .1 Language models and seq2seq

    • MLB11.4.1 -Test your learning

    • ML11-4 .2 Language models and seq2seq

    • MLB11.4.2 -Test your learning

    • ML11-4 .3 Language models and seq2seq

    • ML11-4 .4 Language models and seq2seq

    • ML11-4 .5 Language models and seq2seq

    • MLB11.4.5 -Test your learning

    • ML11-5.1 Image recognition

    • ML11-5.2 Image recognition

    • ML11-5.3 Image recognition

    • MLB11.5 -Test your learning

    • ML11-6.1 Segmentation

    • ML11-6.2 Segmentation

    • ML11-6.3 Segmentation

    • ML11-6.4 Segmentation

    • ML11-6.5 Segmentation

    • MLB11.6 -Test your learning

    • ML11-7 Recommenders

    • MLB11.7 -Test your learning

    • ML11-8.1 Generative models

    • ML11-8.2 Generative models

    • ML11-8.3 Generative models

    • MLB11.8 -Test your learning
  • 9

    You've completed ML Business: Part II!

    • Congrats! here's what's next...