Course Summary
This course is designed to teach you how to build machine learning models using Google Cloud Platform. You will learn how to use TensorFlow, Cloud ML Engine, and other tools to create and train machine learning models.Key Learning Points
- Learn how to build machine learning models using Google Cloud Platform
- Use TensorFlow and Cloud ML Engine to create and train machine learning models
- Gain hands-on experience through exercises and projects
Related Topics for further study
Learning Outcomes
- Understand the fundamentals of machine learning and how to apply it to real-world problems
- Learn how to use Google Cloud Platform tools to build and train machine learning models
- Gain hands-on experience through exercises and projects
Prerequisites or good to have knowledge before taking this course
- Some programming experience in Python
- Familiarity with basic concepts of machine learning
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures and hands-on exercises
- Projects
Similar Courses
- Machine Learning with TensorFlow on Google Cloud Platform
- Applied Data Science with Python
- Data Engineering on Google Cloud Platform
Related Education Paths
- Google Cloud Certified - Professional Data Engineer
- Google Cloud Certified - Professional Cloud Architect
Notable People in This Field
- Co-founder, Coursera
- Senior Fellow, Google
Related Books
Description
What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models.
Knowledge
- Frame a business use case as a machine learning problem.
- Gain a broad perspective of machine learning and where it can be used
- Convert a candidate use case to be driven by machine learning
- Recognize biases that machine learning can amplify.
Outline
- Introduction to Course
- Introduction to Specialization
- Why Google?
- Why Google Cloud?
- Latest from Google
- Introduction to ML on Google Cloud
- What it means to be AI first
- What it means to be AI first
- Two stages of ML
- ML in Google Products
- ML in Google Photos
- Google Translate and Gmail
- Replacing Heuristic Rules
- Pre-trained models
- Machine Learning with Sara Robinson (ML, not rules)
- Vision API in action
- Video intelligence API
- Cloud Speech-to-Text API
- Translation and NL4
- Text-to-Speech
- DialogFlow
- Lab Intro: Pretrained ML APIs Intro
- Lab Solution Invoking Machine Learning APIs
- It's all about data
- A data strategy
- Training and serving skew
- ML Training Phases
- Lab Intro - Framing an ML Problem
- Lab debrief
- Demo: ML in applications
- What it Means to be AI First
- Introduction to AI First
- Pre-trained ML APIs
- All about data
- How Google does ML
- An ML strategy
- Transform your business
- Introduction
- ML Surprise
- The secret sauce
- ML and Business Processes
- End of phases deep dive
- How Google does ML
- Transform your business
- How Google does ML
- Inclusive ML
- Machine Learning and Human Bias
- Evaluating Metrics for Inclusion
- Statistical Measurements and acceptable tradeoffs
- Equality of Opportunity
- Simulating Decisions
- Finding Errors in your dataset using Facets
- Inclusive ML
- Inclusive ML
- Python Notebooks in the cloud
- Module Introduction
- AI Platform Notebooks
- Demo AI Platform Notebooks
- Development process
- Computation and storage
- Lab Intro: Analyzing data using AI Platform Notebooks and BigQuery
- Lab Debrief Analyzing Data using AI Platform Notebooks and BigQuery
- AI Platform Notebooks
- Python Notebooks in the Cloud
- Python Notebooks in the Cloud
- Summary
- Summary
- Resources - Compiled as PDF
- All Quiz Questions on PDF
- Course Slides
Summary of User Reviews
Discover Google's approach to Machine Learning with Coursera's online course. Users have rated this course highly for its comprehensive content, practical exercises, and engaging lectures. One key aspect that many users appreciated was the hands-on experience provided through the use of TensorFlow, a popular ML framework.Pros from User Reviews
- Comprehensive course content
- Engaging lectures
- Practical exercises
- Hands-on experience with TensorFlow
Cons from User Reviews
- Requires prior knowledge of programming and statistics
- Some users found the pace too slow
- Lack of personalized feedback
- Limited interaction with instructors
- Not suitable for advanced learners
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