courseraАнализ данных

Launching into Machine Learning

Курс от Google Cloud

Начальный≈ 14.1 чАнглийский

О курсе

The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

Навыки, которые вы освоите

Model Evaluation Model Training Exploratory Data Analysis Supervised Learning Regression Analysis Classification Algorithms Data Quality Machine Learning Algorithms Sampling (Statistics)Applied Machine Learning Machine Learning Google Cloud Platform Tensorflow Machine Learning Software Data Cleansing Model Optimization

Программа курса

5 модулей · 41 учебных материалов

01Introduction2 материалов

Introduction to Launching into ML

IntroductionВидео Intro to QwiklabsВидео

02Practical ML12 материалов

Introduction to Practical ML

IntroductionВидео

Supervised Learning

Supervised LearningВидео

Учитесь у экспертов

Google Cloud Training

Преподаватель курса

Launching into Machine Learning

О курсе

Навыки, которые вы освоите

Программа курса

Introduction to Launching into ML

Introduction to Practical ML

Supervised Learning

Учитесь у экспертов

Google Cloud Training

ML History

Module Quiz

Introduction to Optimization

Defining ML Models

Introducing Loss Functions

Gradient Descent

TensorFlow Playground

Performance Metrics

Module Quiz

Introduction to Generalization and Sampling

Generalization

Sampling

Demo of Splitting Datasets in BigQuery

Lab: Creating Repeatable Dataset Splits

Lab: Exploring and Creating ML Datasets

Module Quiz

Summary