machine-learning
CtrlK
  • Introduction
    • AI VS ML VS DL
    • Types of Machine Learning
    • Dove raccogliere i dati per l'apprendimento automatico?
    • Importazione di set di dati tramite l'API di Kaggle
  • Material
    • Link
  • Probabilità e statistica
    • Nozione di probabilità con frequenza relativa
    • Valid discrete probability distribution examples
    • Probability with discrete random variable example
    • Mean (expected value) of a discrete random variable
  • Variance and standard deviation of a discrete random variable
  • Continuous random variables
  • Impact of Trasforming ( scaling and shifting ) random variables
  • Chapter Combining random variables
  • Chapter Binomial Variables
  • Chapter Geometric Random Variables
  • More on expected value
  • Chapter Sampling distributions
  • Server Monitoring // Prometheus and Grafana Tutorial
    • Intro
  • Google Colaboratory per python
    • Introduzione a Google Colaboratory
    • Nozioni di Base di Python
    • Numpy in python
    • Pandas
    • Matploit
    • Seaborn
  • Fondamenti di Deep Learning
    • Introduction
    • Neuroni e reti Neurali
    • Keras
    • Modulo 4
  • Esercitazioni
    • Prima Esercitazione
  • Fondamenti di Linux
    • Intro e Comandi
    • Ubuntu Server su Docker
    • Docker Compose vs Dockerfile
  • Books
    • Hands-on Machine Learning With Scikit-learn, Keras, and Tensorflow: Concepts, Tools, and Techniques
      • Ch 1
      • Ch 2
  • Git For Professionals
    • Introduction
  • Ansible
    • Introduzione
  • Virtualization
    • Virtualizzazione
  • Group 1
    • Differenza Ansible Puppet
    • Puppet
  • Group 2
    • Docker
    • Esercitazione
  • Git hub actions
    • Page 1
  • Materiale Corso IA
    • Good Materials
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On this page
  1. Books
  2. Hands-on Machine Learning With Scikit-learn, Keras, and Tensorflow: Concepts, Tools, and Techniques

Ch 2

Look at the big picture.

2. Get the data.

3. Explore and visualize the data to gain insights.

4. Prepare the data for machine learning algorithms.

5. Select a model and train it.

6. Fine-tune your model.

7. Present your solution.

8. Launch, monitor, and maintain your system.

DATA-SET

LogoOpenMLwww.openml.org
LogoFind Open Datasets and Machine Learning Projects | KaggleKaggle
LogoTrending Papers - Hugging Facehuggingface
LogoUCI Machine Learning Repositoryarchive.ics.uci.edu
LogoRegistry of Open Data on AWSregistry.opendata.aws
LogoTensorFlow DatasetsTensorFlow
DataPortals.org - A Comprehensive List of Open Data Portals from Around the Worlddataportals.org
https://opendatamonitor.eu/frontend/web/index.php?r=dashboard%2Findexopendatamonitor.eu
LogoList of datasets for machine-learning researchWikipedia
https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-publicwww.quora.com
LogoDatasetsReddit

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Last updated 14 days ago