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Structuring Machine Learning Projects

This course is a part of Deep Learning, a 5-course Specialization series from Coursera.

You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization.

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deeplearning.ai

Rating 4.6 based on 2,816 ratings
Length 3 weeks
Effort 2 weeks of study, 3-4 hours/week
Starts Aug 12 (6 days ago)
Cost $49
From deeplearning.ai via Coursera
Instructors Andrew Ng, Teaching Assistant - Kian Katanforoosh, Teaching Assistant - Younes Bensouda Mourri, Head Teaching Assistant - Kian Katanforoosh
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming
Tags Data Science Data Analysis Machine Learning

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What people are saying

According to other learners, here's what you need to know

machine learning projects in 73 reviews

Very good overview of the challenges in machine learning projects and pointers to overcoming them.

An Excellent way to manage your machine learning projects .Highly recommended if you in a situation where you stuck what to do next to increase your accuracy Really great Course, learn a lot.

There are not much available online resources to learn about how to structure and manage a Machine Learning projects.

It helps to structure machine learning projects to shorten the time and effort and to prioritize the different alternatives.

wow There are a lot of basics that are necessary to learn Very practical course content along with the very effective machine learning simulator problems I think that this course will give me help about solve problems when i work with machine learning projects.

The topics discussed in this class are very closely associated with the title `Struturing Machine Learning Projects`.

Short and efficient lectures for preparing for structuring machine learning projects in the real world.

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neural network in 42 reviews

Valuable insights into how to structure ai projects with the respect to data, new data, buggy data, synthesized data, mismatched data, and much more such as error analysis and how to use pretrained neural network.

I took this course soon after completing the Machine Learning course, before starting the Neural Network and Deep Learning.

Very good insights on how to go ahead in stuck up projects Having talked to someone who is actively working on Neural Network models, some of the insights I learned from the course looked to be helpful to them as well when we talked.

求中文字幕 Amazing high level overview of neural network alternate implementation options.

A clear explanation of a difficult subject with an emphasis on being able to create and to understand your own neural networks.- Plus in this module how to allocate your resources so you can achieve a successful project.

Not much to learn in this course, basic recommendations can be condensed in one or two lectures Nice presentation on how to start and drive your deep neural network construction.

This course is very helpful that I got to know the things beyond the technical details of the neural network.

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error analysis in 24 reviews

Excellent course, these materials are never taught anywhere, brilliant stuff Very nice error analysis !

Very nice insights on how to assess your ML algorithm using error analysis.

Good course to learn about structuring the projects and carrying out error analysis.

Especially for the error analysis part, you will definitely save much more time in solving these errors than you expected by following the suggestions taught by Andrew.

The concepts are difficult to understand Important insights on error analysis.

Thanks for explaining for Error analysis and how to split train/dev/test data set.

About selecting test and dev set distribution, transfer learning, error analysis , end to end deep learning etc.

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neural networks in 21 reviews

This course follows Neural Networks and Deep Learning and Improving Deep Neural Networks.

A lot of useful tips to create neural networks systematically.

Gives a sense about improving the performance of Deep Neural Networks, with error/bias/variance/data mismatch analysis.

It's very easy now-a-days to create Neural Networks and get a grasp of how they work due to high-level frameworks (keras, scikit, tflearn, etc) and abundance of literature and videos, respectively.

I have in the past build some complex Neural Networks, but would hit road blocks that would ruin productivity for I didn't know how to approach problems correctly, and didn't know what knobs to turn to improve performance of my program.

This course contained a lot of great practical tips for implementing Artificial Neural Networks and structuring your own machine learning and learning projects!

It has been an really awesome experience learning about neural networks from you.

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highly recommended in 15 reviews

Highly recommended :) Coding exercises would be great in this course.

Practical knowledge that tells people what to do next when problem occurs in AI project "Structuring Machine Learning Projects" provide so many good practices in how to correctly implement Deep Learning Models, troubleshoot them and make them better, the tips and recommendations are excellent, highly recommended to anyone interested in deep learning this is a fantastic Course, thanks to everyone that make this Course possible.

Very valuable and highly recommended.

nice simulation of the problems in the quizes Highly recommended to advance ones' career into deep learning Great course !!

Highly recommended!

Highly recommended for professionals who would want to explore deep learning /AI Excellent practical insights for real-world scenarios Great insights and solutions to solve real world deep learning issues very useful and clear.

Highly recommended Interesting lectures.

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prof. andrew ng in 14 reviews

perfect Not so much different with the materials in the Machine Learning course from Prof. Andrew Ng itself.

As Prof. Andrew Ng.

Once again, many thanks to Prof. Andrew Ng for building this course.

details about some implementations how to implement this techniques Full of advice on how to tackle a wide variety of problems common to deep learning projects Another amazing course from prof. andrew Ng.

Now I can't imagine it to be dropped out of the specialization.Thank you prof. Andrew Ng, you gave us a roadmap to move and saved from many blunders on the way.

Personally to Prof. Andrew Ng, thank you for your easy way of explaining things and achieve what I have achieved so far.

Thanks Prof. Andrew Ng for the consistent support of spreading knowledge!

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Coursera

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deeplearning.ai

Rating 4.6 based on 2,816 ratings
Length 3 weeks
Effort 2 weeks of study, 3-4 hours/week
Starts Aug 12 (6 days ago)
Cost $49
From deeplearning.ai via Coursera
Instructors Andrew Ng, Teaching Assistant - Kian Katanforoosh, Teaching Assistant - Younes Bensouda Mourri, Head Teaching Assistant - Kian Katanforoosh
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming
Tags Data Science Data Analysis Machine Learning

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