Machine Learning with Python

Level
Total time
Location
Online
Starting date and place
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Starting dates and places

computer Online: Video conferencing
30 Jun 2021 until 1 Jul 2021
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event June 30, 2021, 10:00-17:00, Video conferencing, Day 1
event July 01, 2021, 10:00-17:00, Video conferencing, Day 2

Description

Introduction

Description will be added soon.

Schedule

Day 1:

  • Machine learning fundamentals
    • Features and labels
    • Training and testing
    • Types of machine learning
  • Scikit-learn API
    • Overview of modules and classes
    • Common process
  • Unsupervised machine learning
    • Outlier detection
    • Clustering
  • Feature engineering
    • Principal Components Analysis
    • Feature selection
    • One-hot encoding
    • Bag-of-words representation
    • TF-IDF

Day 2:

  • Classification
    • K-Nearest Neighbour
    • Decision Tree Classifier
    • Random Forest
    • Neural Network
  • Regression
    • Linear Regression
    • Polynomial Regression
    • Support Vector Regression
  • Model evaluation
    • Measuring performance
    • Overfitting and underfitting
    • Cross validation
    • Model selection
    • Pip…

Read the complete description

Frequently asked questions

There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.

Introduction

Description will be added soon.

Schedule

Day 1:

  • Machine learning fundamentals
    • Features and labels
    • Training and testing
    • Types of machine learning
  • Scikit-learn API
    • Overview of modules and classes
    • Common process
  • Unsupervised machine learning
    • Outlier detection
    • Clustering
  • Feature engineering
    • Principal Components Analysis
    • Feature selection
    • One-hot encoding
    • Bag-of-words representation
    • TF-IDF

Day 2:

  • Classification
    • K-Nearest Neighbour
    • Decision Tree Classifier
    • Random Forest
    • Neural Network
  • Regression
    • Linear Regression
    • Polynomial Regression
    • Support Vector Regression
  • Model evaluation
    • Measuring performance
    • Overfitting and underfitting
    • Cross validation
    • Model selection
    • Pipelines and grid search
  • Where to go from here?

Clients

We’ve previously delivered this workshop at:

  • Jheronimus Academy of Data Science
  • KPN
  • Transavia
  • Vocalink
  • eHealth Africa
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There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.