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Structuring Machine Learning Projects

Approx. 6 hours to complete

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Course Summary

This course is designed to help learners apply machine learning techniques in real-world projects. It covers a variety of topics such as data preparation, model selection, and evaluation of model performance.

Key Learning Points

Learn how to apply machine learning techniques to real-world projects
Discover how to prepare data, select models, and evaluate model performance
Gain hands-on experience with machine learning tools and technologies

Learning Outcomes

Apply machine learning techniques to real-world projects
Prepare data, select models, and evaluate model performance
Gain hands-on experience with machine learning tools and technologies

Prerequisites or good to have knowledge before taking this course

Basic understanding of machine learning concepts
Familiarity with programming languages such as Python

Course Difficulty Level

Intermediate

Course Format

Self-paced
Online
Hands-on
Project-based

Similar Courses

Applied Machine Learning
Advanced Machine Learning

Related Education Paths

Notable People in This Field

AI expert
AI researcher

Related Books

Description

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.

By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

Outline

ML Strategy (1)
Why ML Strategy
Orthogonalization
Single Number Evaluation Metric
Satisficing and Optimizing Metric
Train/Dev/Test Distributions
Size of the Dev and Test Sets
When to Change Dev/Test Sets and Metrics?
Why Human-level Performance?
Avoidable Bias
Understanding Human-level Performance
Surpassing Human-level Performance
Improving your Model Performance
Andrej Karpathy Interview
Connect with your Mentors and Fellow Learners on Discourse!
Lectures in PDF
Machine Learning Flight Simulator
Bird Recognition in the City of Peacetopia (Case Study)

ML Strategy (2)
Carrying Out Error Analysis
Cleaning Up Incorrectly Labeled Data
Build your First System Quickly, then Iterate
Training and Testing on Different Distributions
Bias and Variance with Mismatched Data Distributions
Addressing Data Mismatch
Transfer Learning
Multi-task Learning
What is End-to-end Deep Learning?
Whether to use End-to-end Deep Learning
Ruslan Salakhutdinov Interview
Lectures in PDF
Acknowledgments
Autonomous Driving (Case Study)

Summary of User Reviews

Discover the exciting world of machine learning with this comprehensive course on Coursera. Students have praised the course for its hands-on approach and easy-to-follow explanations. Many users have also found the course to be an excellent resource for building real-world projects.

Key Aspect Users Liked About This Course

Hands-on approach

Pros from User Reviews

Easy-to-follow explanations
Excellent resource for building real-world projects
In-depth coverage of machine learning concepts
Engaging assignments and quizzes
Great for beginners and experienced learners alike

Cons from User Reviews

Some sections may be too technical for beginners
Course material can be a bit dry at times
Limited interaction with the instructor
No certificate of completion for the audit option
Some assignments may be too challenging for beginners

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