Who is the course for?

  • All Tech profiles interested in learning basic concepts of ML

  • Engineers & data scientists with python knowledge

  • Content only available for Adevinta employees

Adevinta Avenues Competencies

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

Course Structure

  • 8 core modules

    Go at your own pace

  • 6 hours of tailored content

    Beginners level introduction to ML for tech

  • Sharing ideas

    Discussion boards & quarterly ML Hackathons

Course curriculum

  • 1

    Welcome to Machine Learning for coders I

    • Welcome to Machine Learning for coders I

  • 2

    Adevinta ML Strategy

    • A message from Renaud Bruyeron, Adevinta CPTO

  • 3

    Introduction to Machine Learning

    • Meet your instructor - A message from Francesco Mosconi

    • 01- Machine Learning and AI

    • MLT-01.1- Test your learning

    • 02- Machine Learning Enablers

    • MLT-01.2- Test your learning

  • 4

    Intro to Machine Learning- Dealing with data

    • 03 - Tabular Data

    • MLT-01.3- Test your learning

    • 04-Data Operations in Pandas

    • MLT-01.4- Test your learning

    • 05 -Data Structures

    • MLT-01.5- Test your learning

    • 06 -Input and Output

    • MLT-01.6- Test your learning

    • 07- Selections and Filters

    • MLT-01.7- Test your learning

    • 08 - Feature Engineering

    • MLT-01.8- Test your learning

    • 09- Aggregations

    • MLT-01.9- Test your learning

    • 10- Sort & Pivot

    • MLT-01.10- Test your learning

    • 11- Joins

    • MLT-01.11- Test your learning

    • 12 -Time Series

    • MLT-01.12- Test your learning

    • 13 - Other Commands

    • MLT-01.13- Test your learning

    • 14- Data Visualization

    • MLT-01.14- Test your learning

    • 15 -Common questions

    • 16-How to do the labs

    • 17- Lab Walkthrough

    • 18- Lab Exercise 1

  • 5

    Regression

    • 00- The 3 main techniques in machine learning

    • MLT-02.0- Test your learning

    • 01- Types of machine learning

    • MLT-02.1- Test your learning

    • 02- Supervised and Unsupervised Learning

    • MLT-02.2- Test your learning

    • 03- How to choose ML technique.

    • MLT-02.3- Test your learning

    • 04-Regression

    • MLT-02.4- Test your learning

    • 05- Loss minimization

    • MLT-02.5- Test your learning

    • 06- Machine Learning Workflow

    • MLT-02.6- Test your learning

    • 07-The R Square Score

    • MLT-02.7- Test your learning

    • 08-Generalization and Overfitting

    • MLT-02.8- Test your learning

    • 09 Scikit Learn

    • 10- Scikit Learn Components

    • MLT-02.10- Test your learning

    • 11- Scikit Learn Pipelines

    • 12- Scikit Learn Regression Models

    • 13 -Common questions

    • 14- Lab Walkthrough

    • 15- Lab Exercise 1

    • 16- Lab Exercise 2

    • 17- Lab Exercise 3

    • 18-Jupyter debug magic

  • 6

    Classification

    • 00 Classification

    • 01 Classification Labels

    • MLT-03.1- Test your learning

    • 02 Binary Classification with Decision Tree

    • MLT-03.2- Test your learning

    • 03 Advantages of Decision Trees

    • MLT-03.3- Test your learning

    • 04 History of ML models

    • 05 KNearest Neighbors

    • MLT-03.5- Test your learning

    • 06 Logistic Regression

    • MLT-03.6- Test your learning

    • 07 Neural Networks

    • MLT-03.7- Test your learning

    • 08 Support Vector Machines

    • MLT-03.8- Test your learning

    • 09 Ensembles and bagging

    • MLT-03.9- Test your learning

    • 10 Random Forest and Boosting

    • MLT-03.10- Test your learning

    • 11 Scikit Learn

    • 12 Model Evaluation

    • MLT-03.12- Test your learning

    • 13 Confusion Matrix

    • MLT-03.13- Test your learning

    • 14 Multi-class Classification

    • 15 -Common questions

    • 16- Lab Walkthrough

    • 17- Lab Exercise 1

    • 19- Lab Exercise 3

  • 7

    Clustering

    • 00- Introduction to clustering

    • 01- Distance and Similarity

    • MLT-04.01- Test your learning

    • 02- K-Means

    • MLT-04.02 Test your learning

    • 03- Model Evaluation

    • MLT-04.03- Test your learning

    • 04- Elbow Method

    • MLT-04.04- Test your learning

    • 05- Silhouette Score

    • MLT-04.05- Test your learning

    • 06-Other clustering methods

    • MLT-04.06- Test your learning

    • 07-Scikit Learn clustering

    • MLT-04.0-7 Test your learning

    • 08-Scikit Learn implementation

    • MLT-04.08- Test your learning

    • 09 -Common questions

    • 10- Lab Walkthrough

    • 12- Lab Exercise 2

    • 13- Lab Exercise 3

  • 8

    Feature Engineering

    • 00- Feature Engineering

    • MLT-05.0- Test your learning

    • 01- Missing Data

    • MLT-05.1- Test your learning

    • 02- How to deal with missing data

    • MLT-05.2- Test your learning

    • 03-Standardization and Normalization

    • MLT-05.3- Test your learning

    • 04- Categorical Features.

    • MLT-05.4- Test your learning

    • 05-High cardinality features

    • MLT-05.5- Test your learning

    • 06- Feature selection

    • MLT-05.6- Test your learning

    • 07- Scikit Learn

    • MLT-05.7- Test your learning

    • 08- Common questions

    • MLT-05.8- Test your learning

    • 09- Lab Walkthrough

    • 10- Lab Exercise 1

    • 11- Lab Exercise 2

    • 12- Lab Exercise 3

  • 9

    Model Evaluation

    • 00- Introduction

    • 01- Baselines.

    • MLT-06.1- Test your learning

    • 02- An anecdote on baselines

    • 03- Performance

    • MLT-06.3- Test your learning

    • 04- Crossvalidation

    • MLT-06.4- Test your learning

    • 05- Train Validation Test split

    • MLT-06.5- Test your learning

    • 06-ROC Curve

    • MLT-06.6- Test your learning

    • 07- Learning Curves

    • MLT-06.7- Test your learning

    • 08- Dimensionality Reduction

    • MLT-06.8- Test your learning

    • 09 - t-SNE

    • MLT-06.9- Test your learning

    • 10- Scikit Learn Pipelines

    • MLT-06.10- Test your learning

    • 11- Scikit Learn Flowchart

    • 12- Common questions

    • 13- Lab Walkthrough

    • 14- Lab Exercise 1

    • 15- Lab Exercise 2

    • 16- Lab Exercise 3

  • 10

    You've completed ML for coders : Part I!

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

    • Congrats! Here's what's next...