Adevinta ML Academy

Introduction to the training, by Rosangela Fonseca

Content and structure, by Manuel Sanchez

Course check-in

Meet your instructor - A message from Tom Gadsby

MLB01 -Test your learning- Overview of Machine Learning

What is a Machine Learning Model?

MLB01 -Test your learning- What is a Machine Learning Model?

Categories of Machine Learning

MLB01 -Test your learning- Categories of Machine Learning

How does a Model Actually Learn?

MLB01 -Test your learning- How does a Model Actually learn?

The Machine Learning Pipeline

MLB01 -Test your learning- The Machine Learning Pipeline

A recap from previous lesson by Manuel Sanchez

Introduction- A message from Tom Gadsby

Overview of ML Performance

MLB02-Test your learning- Overview of ML Performance

Classification Model Performance

MLB02- Test your learning- Classification Model Performance

Introduction- A message from Tom Gadsby

Regression Model Performance

MLB03-Test your learning- Regression Model Performance

Clustering Model Performance

MLB03- Test your learning- Clustering Model Performance

Orange for ML

Applying your knowledge in Orange

MLB04 - Test your learning- Applying your knowledge in Orange

Data Preparation

MLB04 -Test your learning- Data Preparation

Model Training & Testing

MLB04 - Test your learning- Model Training & Testing

COMPETITION TIME!

Say it like it it- Course Survey

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

09- Conclusion - Raquel Sainz

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

00- Introduction to Data in Adevinta

1.1- Introduction to data at Adevinta

1.2 - Sharing Data in Adevinta , Data Highway

1.3 - Metrics

1.4 - Recap

MLB08 - Test your learning- Lesson 1

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

MLB08 - Test your learning- Lesson 2

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

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

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

MLB08 - Test your learning- Lesson 5

00- Introduction to Privacy in Adevinta

MLB08.1 - Test your learning- Lesson 1

01- Personal Data

MLB08.1 - Test your learning- Lesson 2

02- Privacy: Purpose

MLB08.1 - Test your learning- Lesson 3

03- Privacy: User rights

MLB08.1 - Test your learning- Lesson 4

04- Privacy: Incidents

MLB08.1 - Test your learning- Lesson 5

05- Privacy: Anonymous

MLB08.1 - Test your learning- Lesson 6

06- Privacy: Recap

07- Privacy in Machine Learning - part I

MLB08.1 - Test your learning- Lesson 8

08- Privacy: Minimization techniques

MLB08.1 - Test your learning- Lesson 9

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

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

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

Congrats! Here's what's next...