À propos de ce cours : Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation. Course Objectives: Identify why deep learning is currently popular Optimize and evaluate models using loss functions and performance metrics Mitigate common problems that arise in machine learning Create repeatable and scalable training, evaluation, and test datasets
Les destinataires de ce cours : This course is aimed at data scientists, data engineers and programmers interested in learning how to apply machine learning in practice.
Créé par : Google Cloud
| Informations de base | Cours 2 de 5 dans Machine Learning with TensorFlow on Google Cloud Platform Specialization |
| Niveau | Intermédiaire |
| Engagement | 5 - 7 hours per week |
| Langue | English, Sous-titres : French, Portuguese (Brazilian), German, Spanish, Japanese |
| Matériel requis | You'll need to use a desktop web browser to access labs. |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Prévisualiser
Programme de cours
SEMAINE 1
Introduction
In this course you’ll get foundational ML knowledge so that you understand the terminology that we use throughout the specialization. You will also learn practical tips and pitfalls from ML practitioners here at Google and walk away with the code and the knowledge to bootstrap your own ML models.
2 vidéos
- Vidéo: Intro to Qwiklabs
Practical ML
In this module, we will introduce some of the main types of machine learning and review the history of ML leading up to the state of the art so that you can accelerate your growth as an ML practitioner.
10 vidéos
- Vidéo: Supervised Learning
- Vidéo: Regression and Classification
- Vidéo: Short History of ML: Linear Regression
- Vidéo: Short History of ML: Perceptron
- Vidéo: Short History of ML: Neural Networks
- Vidéo: Short History of ML: Decision Trees
- Vidéo: Short History of ML: Kernel Methods
- Vidéo: Short History of ML: Random Forests
- Vidéo: Short History of ML: Modern Neural Networks
- Discussion Prompt: Modern Neural Networks
Noté: Module Quiz
Optimization
In this module we will walk you through how to optimize your ML models.
13 vidéos
- Vidéo: Defining ML Models
- Vidéo: Introducing the Natality Dataset
- Vidéo: Introducing Loss Functions
- Vidéo: Gradient Descent
- Vidéo: Troubleshooting a Loss Curve
- Vidéo: ML Model Pitfalls
- Vidéo: Lab: Introducing the TensorFlow Playground
- Vidéo: Lab: TensorFlow Playground - Advanced
- Vidéo: Lab: Practicing with Neural Networks
- Vidéo: Loss Curve Troubleshooting
- Vidéo: Performance Metrics
- Vidéo: Confusion Matrix
Noté: Module Quiz
Generalization and Sampling
Now it’s time to answer a rather weird question: when is the most accurate ML model not the right one to pick? As we hinted at in the last module on Optimization -- simply because a model has a loss metric of 0 for your training dataset does not mean it will perform well on new data in the real world.
9 vidéos
- Vidéo: Generalization and ML Models
- Vidéo: When to Stop Model Training
- Vidéo: Creating Repeatable Samples in BigQuery
- Vidéo: Demo: Splitting Datasets in BigQuery
- Vidéo: Lab Introduction
- Vidéo: Lab Solution Walkthrough
- Vidéo: Lab Introduction
- Vidéo: Lab Solution Walkthrough
Noté: Lab: Creating Repeatable Dataset Splits in BigQuery
Noté: Lab: Exploring and Creating ML Datasets
Noté: Module Quiz
Summary
1 vidéo
FAQ
Comment cela fonctionne
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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Créateurs
Google Cloud
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
Fluent flow of introduction using examples. Gives an overview of the ML process concepts and tips. Detailed concepts are only mentioned quickly, so to fully benefit from the course would probably require some prior experience in ML. Valuable insights and summary from experienced ML engineer are provided in this course.
Nice course. Starting from basics showing concepts clearly. I really liked it.
A good introduction to the process. Well structured. In particular I liked the section on loss functions.
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A very good introduction to ML and to important tools to use on the creating and tuning and optimization of ML models.
Thank you.