Who is the course for?

  • Tech profiles interested in knowing deep concepts of ML

  • People who want experience with real ML cases

  • Engineers with python knowledge

  • People who have completed ML for Coders I

  • Content only available for Adevinta employees

Course Structure

  • 8 core modules

    Go at your own pace

  • 4.5 hours of tailored content

    Advanced course for coders

  • Sharing ideas

    Be part of our ML community & ML Hackathons

Course curriculum

  • 1

    Welcome to Machine Learning for coders II

    • Welcome to Machine Learning for coders II
  • 2

    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
  • 3

    Data in Adevinta (Advanced)

    • 3.1 & 3.2 -Data quality dimensions and Data quality and lineage (Optional)

    • 3.3 -The harm of bad data

    • 3.4 -Why does low quality data happen?

    • 3.5 - Data quality lifecycle

    • MLT-8.3- Test your learning

    • 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

    • MLT-8.4- Test your learning

    • ML8- 5 - BONUS CONTENT ondata in monoliths, and more on mapping

    • MLT-8.5- Test your learning
  • 4

    Privacy in Adevinta

    • 00- Introduction to Privacy in Adevinta

    • MLT-08.0 Test your learning

    • 01- Personal Data

    • MLT-08.1- Test your learning

    • 02- Privacy: Purpose

    • MLT-08.2- Test your learning

    • 03- Privacy: User rights

    • MLT-08.3- Test your learning

    • 04- Privacy: Incidents

    • MLT-08.4- Test your learning

    • 05- Privacy: Anonymous

    • MLT-08.5- Test your learning

    • 06- Privacy: Recap

    • 07- Privacy in Machine Learning - part I

    • MLT-08.7- Test your learning

    • 08- Privacy: Minimization techniques

    • MLT-08.8- Test your learning

  • 5

    Natural language processing

    • 00- Introduction to NLP

    • MLT-10.0- Test your learning

    • 01- Machine Learning on Text

    • MLT-10.1- Test your learning

    • 02- Bag of Words

    • MLT-10.2- Test your learning

    • 03- Features from text

    • MLT-10.3- Test your learning

    • 04-Text Vectorizers

    • MLT-10.4- Test your learning

    • 05- Lab 1 Walktrhough

    • 06- Lab 1 Exercise 1

    • 07- Lab 1 Exercise 2

    • 08- Natural Language Processing

    • MLT-10.8- Test your learning

    • 09- Lab 2 Walktrhough

    • 10- Lab 2 Exercise 1

    • 11- Lab 2 Exercise 2

    • 12 -Word Embeddings

    • MLT-10.12- Test your learning

    • 13- Self-supervised Learning.

    • MLT-10.13- Test your learning

    • 14 - Word2Vec and Glove

    • MLT-10.14- Test your learning

    • 15- Lab 3 Walkthroug

    • 16 - Lab 3 Exercise 1
  • 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- Inmmaturity 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

    Productionising a ML model - Part I

    • 01- Going to production

    • MLT-11.1- Test your learning

    • 02- Scaling & performance- vertically or horizontally

    • 03 - Scaling & performance- caching & Gpu's

    • MLT-11.2- Test your learning

  • 8

    Productionising a ML model - Part II

    • 01- Deploy your model into common platform- Lesson 1

    • 02- Deploy your model into common platform- Lesson 2

    • 03- Deploy your model into common platform- Lesson 3

    • 04- Deploy your model into common platform- Lesson 4

    • 05- Deploy your model into common platform- Lesson 5

    • 06- Deploy your model into common platform- Lesson 6

    • 07- Deploy your model into common platform- Lesson 7

    • 08- Deploy your model into common platform- Lesson 8
  • 9

    Real world Machine Learning

    • ML12- Tech 00 Lab Exercise 1

    • ML12- Tech 01 Lab Exercise 1 Solution

    • ML12- Tech 02 Lab Exercise 2

    • ML12- Tech 03 Lab Exercise 2 Solution

    • ML12- Tech 04 Lab Exercise 3

    • ML12- Tech 05 Lab Exercise 3 Solution

    • ML12- Tech 08 Lab Exercise 5

    • ML12- Tech 09 Lab Exercise 5 Solution

    • ML12- Tech 10 Lab Exercise 6

    • ML12- Tech 11 Lab Exercise 6 Solution

    • ML12- Tech 12 Lab Exercise 7

    • ML12- Tech 13 Lab Exercise 7 Solution

    • ML12- Tech 15 Lab Exercise 8 Solution

    • ML12- Tech 16 Lab Exercise 9

    • ML12- Tech 17 Lab Exercise 9 Solution

    • ML12- Tech 18 Lab Exercise 10

    • ML12- Tech 19 Lab Exercise 10 Solution

    • ML12- Tech 20 Lab Exercise 11

    • ML12- Tech 21 Lab Exercise 11 Solution

    • ML12- Tech 22 Lab Exercise 12 Part 1

    • ML12- Tech 23 Lab Exercise 12 Part 1 Solution

    • ML12- Tech 24 Lab Exercise 12 Part 2

    • ML12- Tech 25 Lab Exercise 12 Part 2 Solution

    • ML12- Tech 26 Lab Exercise 13

    • ML12- Tech 27 Lab Exercise 13 Solution

    • ML12- Tech 28 Lab Exercise 14

    • ML12- Tech 29 Lab Exercise 14 Solution

    • ML12- Tech 30 Lab Exercise 15

    • ML12- Tech 31 Lab Exercise 15 Solution

    • ML12- Tech 32 Lab Exercise 16

    • ML12- Tech 33 Lab Exercise 16 Solution

    • ML12- Tech 34 Lab Exercise 17

    • ML12- Tech 35 Lab Exercise 17 Solution

    • ML12- Tech 36 Lab Exercise 18

    • ML12- Tech 37 Lab Exercise 18 Solution

    • ML12- Tech 38 Lab Exercise 19

    • ML12- Tech 39 Lab Exercise 19 Solution

    • Tech 40 Lab Exercise 20

    • ML12- Tech 41 Lab Exercise 20 Solution

    • ML12- Tech 42 Lab Next Steps

    • ML12- Tech 43 Lab 2 Walkthrough

    • ML12- Tech 44 Lab 2 Solution Walkthrough

    • ML12- Tech 45 Lab 2 Solution Ideas Implementation

    • ML12- Tech 46 Lab 2 Solution Bonus Embeddings
  • 10

    You've completed ML for coders : Part II!

    • You've completed ML for coders : Part II!

    • Congrats! Here's what's next...

Adevinta Avenues Competencies

At the end of this course you will have worked on the following competencies: Self Starter, Curious Analyst, Technology Advising/ Consulting, Resource Planning & Optimization

Adevinta Avenues