Course Summary
This course teaches you how to launch machine learning models in production. It covers topics such as deploying models, managing models, and monitoring models.Key Learning Points
- Learn best practices for deploying machine learning models in production
- Understand how to manage and monitor machine learning models
- Get hands-on experience with deploying models using Flask and Docker
Job Positions & Salaries of people who have taken this course might have
- USA: $112,000
- India: ₹13,00,000
- Spain: €46,000
- USA: $112,000
- India: ₹13,00,000
- Spain: €46,000
- USA: $117,000
- India: ₹15,00,000
- Spain: €49,000
- USA: $112,000
- India: ₹13,00,000
- Spain: €46,000
- USA: $117,000
- India: ₹15,00,000
- Spain: €49,000
- USA: $135,000
- India: ₹18,00,000
- Spain: €59,000
Related Topics for further study
Learning Outcomes
- Learn how to deploy machine learning models in production
- Gain hands-on experience with managing and monitoring models
- Understand best practices for deploying models using Flask and Docker
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of machine learning
- Experience with Python programming
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Hands-on projects
Similar Courses
- Applied Machine Learning
- Advanced Machine Learning
Related Education Paths
Notable People in This Field
- Andrew Ng
- Karthik Ramasamy
Related Books
Description
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.
Outline
- Introduction to Course
- Intro to Course
- Getting Started with Google Cloud and Qwiklabs
- Improve Data Quality and Exploratory Data Analysis
- Introduction
- Improve Data Quality
- Lab Intro Improve Data Quality
- Exploratory Data Anlaysis
- Lab Intro Exploratory Data Analysis
- Resources
- Practice Quiz on Improve Data Quality
- Practice Quiz on Exploratory Data Analysis
- Practical ML
- Introduction
- Supervised Learning
- Regression and Classification
- Short History of ML: Linear Regression
- Short History of ML: Perceptron
- Short History of ML: Neural Networks
- Lab Intro: Introduction to Linear Regression
- Lab Intro: Introduction to Logistic Regression
- Short History of ML: Decision Trees
- Short History of ML: Random Forests
- Lab Intro: Decision Trees and Random Forests in Python
- Short History of ML: Kernel Methods
- Short History of ML: Modern Neural Networks
- Resources
- Supervised Learning
- Regression and Classification
- Linear Regression
- Perceptron
- Neural Networks
- Decision Trees
- Kernel Methods
- History of ML: Modern Neural Networks
- Optimization
- Introduction
- Defining ML Models
- Introducing the Course Dataset
- Introduction Loss Functions
- Gradient Descent
- Troubleshooting Loss Curves
- ML Model Pitfalls
- Lecture Lab: Introducing the TensorFlow Playground
- Lecture Lab: TensorFlow Playground - Advanced
- Lecture Lab: Practicing with Neural Networks
- Loss Curve Troubleshooting
- Performance Metrics
- Confusion Matrix
- Resources
- Lesson Quiz
- Lesson Quiz
- Lesson Quiz
- Module Quiz
- Generalization and Sampling
- Introduction
- Generalization and ML Models
- When to Stop Model Training
- Lecture Creating Repeatable Samples in BigQuery
- LectureDemo: Splitting Datasets in BigQuery
- Lab Introduction Creating Repeatable Dataset Splits in BigQuery
- Lab Solution Walkthrough Creating Repeatable Dataset Splits in BigQuery
- Lab Introduction Exploring and Creating ML Datasets
- Lab Solution Walkthrough Exploring and Creating ML Datasets
- Resources
- Generalization and ML Models
- Module Quiz
- Summary
- Course Summary
- Resources - Readings Compiled as PDF
- Quiz Questions as a PDF
- Course Slides
- Course Quiz
Summary of User Reviews
Learn the essentials of machine learning with this comprehensive course on Coursera. Students have praised the course for its knowledgeable instructors and easy-to-follow lessons. One key aspect that many users thought was good is the course's emphasis on hands-on projects and real-world applications.Pros from User Reviews
- Instructors are knowledgeable and engaging
- Lessons are easy to follow and understand
- Course emphasizes hands-on projects and real-world applications
- Great preparation for a career in machine learning or data science
Cons from User Reviews
- Some users found the course material to be too basic
- Course may not be suitable for those without a background in math or programming
- Some users experienced technical difficulties with the platform
- Course may be too time-consuming for those with busy schedules
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