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Feature Engineering - Italiano

Google Cloud Training

Questo corso illustra i vantaggi dell'utilizzo di Vertex AI Feature Store, come migliorare l'accuratezza dei modelli di ML e come trovare le colonne di dati che forniscono le caratteristiche più utili. Il corso include inoltre contenuti e lab sul feature engineering utilizzando BigQuery ML, Keras e TensorFlow.

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What's inside

Syllabus

Introduzione
Questo modulo fornisce una panoramica del corso e dei suoi obiettivi.
Introduzione a Vertex AI Feature Store
Questo modulo introduce Vertex AI Feature Store.
Read more
Dai dati non elaborati alle caratteristiche
Il feature engineering è spesso la fase più lunga e difficile della creazione del tuo progetto di ML. Nel processo di feature engineering, inizi con i tuoi dati non elaborati e utilizzi la tua conoscenza del dominio per creare caratteristiche che faranno funzionare i tuoi algoritmi di machine learning. In questo modulo esploriamo gli elementi che rendono valida una caratteristica e come rappresentarli nel modello di ML.
Feature engineering
Questo modulo analizza le differenze tra machine learning e statistiche e come eseguire il feature engineering sia in BigQuery ML che in Keras. Tratteremo anche alcune pratiche avanzate di feature engineering.
Pre-elaborazione e creazione di caratteristiche
In questo modulo imparerai di più su Dataflow, che è una tecnologia complementare ad Apache Beam: entrambi possono aiutarti a creare ed eseguire preelaborazione e feature engineering.
Incroci di caratteristiche: TensorFlow Playground
Nel machine learning tradizionale, gli incroci di caratteristiche non svolgono un ruolo importante, ma nei metodi di ML moderni, sono una parte inestimabile del tuo toolkit. In questo modulo imparerai a riconoscere i tipi di problemi in cui gli incroci di caratteristiche rappresentano un modo efficace per aiutare le macchine ad apprendere.
Introduzione a TensorFlow Transform
TensorFlow Transform (tf.Transform) è una libreria per la pre-elaborazione dei dati con TensorFlow. tf.Transform è utile per la pre-elaborazione che richiede un passaggio completo dei dati, come ad esempio: normalizzazione di un valore di input tramite mean e stdev; integrazione di un vocabolario esaminando tutti gli esempi di input per valori; suddivisione in bucket degli input in base alla distribuzione dei dati osservati. In questo modulo esploreremo i casi d'uso per tf.Transform.
Riepilogo
Questo modulo è un riepilogo del corso Feature Engineering.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Delve into advanced concepts like incroci di caratteristiche, TensorFlow Transform, e pre-elaborazione, utili per sviluppare soluzioni di ML complesse
Impara dalle istruzioni dettagliate degli esperti di Google Cloud, fornendo una comprensione approfondita di Vertex AI Feature Store e delle sue funzionalità
Sviluppa competenze pratiche in tecniche avanzate di feature engineering, tra cui feature engineering in BigQuery ML, Keras e TensorFlow
Applica le conoscenze acquisite attraverso i lab pratici, rinforzando i concetti teorici e fornendo esperienza pratica
Questo corso è progettato per coloro che cercano di migliorare i propri modelli ML e acquisire una profonda comprensione del feature engineering
Potrebbe essere necessario avere una conoscenza pregressa di machine learning e feature engineering per ottenere il massimo da questo corso

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Feature Engineering - Italiano with these activities:
Organize and review course materials
Stay organized and enhance retention by compiling and reviewing notes, assignments, quizzes, and exams, ensuring a comprehensive grasp of the course content.
Show steps
  • Gather all course materials, including notes, assignments, quizzes, and exams.
  • Organize the materials systematically, creating a logical structure.
  • Review the materials regularly, highlighting key concepts and summarizing important points.
Rivedi concetti matematici fondamentali
Rivedere i concetti matematici fondamentali aiuterà a rafforzare le basi necessarie per l'apprendimento del feature engineering in questo corso.
Browse courses on Algebra
Show steps
  • Rivedere i concetti di algebra lineare
  • Ripassare le derivate e gli integrali
  • Esercitarsi nella risoluzione di equazioni differenziali
Review foundational data science concepts
Review basic statistical concepts like descriptive statistics, probability theory, and hypothesis testing to reinforce foundational knowledge.
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Show steps
  • Go over key concepts of descriptive statistics like mean, median, mode, range, and variance.
  • Dive deep into probability theory covering concepts like conditional probability, Bayes' theorem, and random variables.
  • Explore advanced statistical concepts like hypothesis testing techniques, including t-tests, ANOVA, and chi-square tests.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Compile a Glossary of Feature Engineering Terms
Compile a glossary of feature engineering terms to improve your understanding of the terminology used in the field.
Browse courses on Feature Engineering
Show steps
  • Research feature engineering terms
  • Create a list of terms and their definitions
  • Organize the terms into categories
  • Write a brief explanation of each term
Join a study group or online forum
Engage with other learners by joining a study group or online forum, enabling you to exchange knowledge, share insights, and collectively enhance your understanding.
Show steps
  • Identify relevant study groups or online forums related to Feature Engineering.
  • Join the group or forum and actively participate in discussions.
  • Ask questions, share your knowledge, and engage in constructive debates.
Practice Feature Engineering in BigQuery ML
Practice feature engineering in BigQuery ML to improve your understanding of data preparation and feature selection.
Browse courses on Feature Engineering
Show steps
  • Create a BigQuery dataset and table
  • Load data into the table
  • Explore the data
  • Select features
  • Preprocess features
  • Create a model using the engineered features
Explore Feature Engineering tutorials
Supplement your understanding of Feature Engineering by following tutorials that provide practical examples and explore advanced techniques.
Browse courses on Feature Engineering
Show steps
  • Find tutorials on platforms like Coursera, edX, or YouTube.
  • Choose tutorials that align with your skill level and interests.
  • Follow the tutorials step-by-step, taking notes and experimenting with the code.
  • Apply the techniques learned to your own projects or datasets.
Seguire tutorial sul feature engineering
Seguire tutorial per migliorare le abilità di feature engineering
Browse courses on Feature Engineering
Show steps
  • Identificare tutorial appropriati
  • Seguire i passaggi descritti nei tutorial
  • Esercitarsi con i propri dati
Complete Keras and TensorFlow Feature Engineering Tutorials
Complete tutorials on feature engineering using Keras and TensorFlow to enhance your understanding of advanced feature engineering techniques.
Browse courses on Feature Engineering
Show steps
  • Follow the Keras feature engineering tutorial
  • Follow the TensorFlow feature engineering tutorial
  • Experiment with different feature engineering techniques
  • Apply the techniques to real-world datasets
Practice BigQuery ML
Enhance your proficiency in BigQuery ML through repetitive exercises, solidifying your understanding of its features and capabilities.
Browse courses on BigQuery ML
Show steps
  • Set up a BigQuery ML environment and familiarize yourself with its interface.
  • Create datasets, tables, and models using BigQuery ML.
  • Experiment with different feature engineering techniques provided by BigQuery ML.
  • Evaluate model performance and troubleshoot any issues encountered.
  • Build and deploy machine learning models using BigQuery ML.
Esercizi di feature engineering
Eseguire esercizi di feature engineering per rinforzare la comprensione dei concetti
Browse courses on Feature Engineering
Show steps
  • Trovare e caricare insiemi di dati pertinenti
  • Applicare tecniche di feature engineering
  • Valutare l'efficacia delle caratteristiche generate
Create a Feature Engineering Project in Dataflow
Create a project in Dataflow to apply feature engineering techniques on a large dataset, reinforcing your understanding of distributed data processing and feature engineering.
Browse courses on Feature Engineering
Show steps
  • Design the feature engineering pipeline
  • Implement the pipeline in Dataflow
  • Run the pipeline
  • Analyze the results
  • Write a report on the project
Progetto sul feature engineering
Creare un progetto per testare e consolidare le conoscenze acquisite sul feature engineering
Browse courses on Feature Engineering
Show steps
  • Definire l'obiettivo del progetto
  • Raccogliere e preparare i dati
  • Applicare tecniche di feature engineering
  • Valutare i risultati
Build a feature store using Vertex AI
Showcase your understanding by creating a comprehensive feature store using Vertex AI, demonstrating your ability to apply the concepts learned in this course.
Browse courses on Feature Engineering
Show steps
  • Plan and design your feature store, outlining the data sources and features to be included.
  • Implement data pipelines to extract and transform data from various sources.
  • Create and manage features within the Vertex AI Feature Store.
  • Monitor and evaluate the performance of your feature store.
  • Integrate your feature store with machine learning models.

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Course image
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Session
August 19, 2024 - October 21, 2024
Language
Italiano
Transcript
English
Good to know
Delve into advanced concepts like incroci di caratteristiche, TensorFlow Transform, e pre-elaborazione, utili per sviluppare soluzioni di ML complesse
Impara dalle istruzioni dettagliate degli esperti di Google Cloud, fornendo una comprensione approfondita di Vertex AI Feature Store e delle sue funzionalità
Sviluppa competenze pratiche in tecniche avanzate di feature engineering, tra cui feature engineering in BigQuery ML, Keras e TensorFlow
Applica le conoscenze acquisite attraverso i lab pratici, rinforzando i concetti teorici e fornendo esperienza pratica
Questo corso è progettato per coloro che cercano di migliorare i propri modelli ML e acquisire una profonda comprensione del feature engineering
Potrebbe essere necessario avere una conoscenza pregressa di machine learning e feature engineering per ottenere il massimo da questo corso
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