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学生对 deeplearning.ai 提供的 Advanced Learning Algorithms 的评价和反馈

4.9
41 个评分
6 条评论

课程概述

In the second course of the Machine Learning Specialization, you will: • Build and train a neural network with TensorFlow to perform multi-class classification • Apply best practices for machine learning development so that your models generalize to data and tasks in the real world • Build and use decision trees and tree ensemble methods, including random forests and boosted trees The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key theoretical concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start....

热门审阅

MM

Jun 22, 2022

Excellent course, although it would have been good to talk more about backward propagation, after finishing this course this is the only point that is left unclear in my mind.

WH

Jun 18, 2022

An excellent update to the previous Machine Learning course. Goes into excellent detail about each algorithm and the practical notebooks are useful and easy to follow.

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1 - Advanced Learning Algorithms 的 11 个评论(共 11 个)

创建者 Mohamed N M

Jun 23, 2022

E​xcellent course, although it would have been good to talk more about backward propagation, after finishing this course this is the only point that is left unclear in my mind.

创建者 Changlin F

Jun 22, 2022

Seems lacking some mathematical details like how to calculate Backpropagation this time

创建者 Yuriy G

Jul 1, 2022

Slightly disappointed with the assignments to be honest, most of them are too easy to solve, and moreover can be just copypasted from the hints.

Great theory which lacks some demanding practice tasks.

创建者 rcotta

Jun 28, 2022

Course 2 of 3 from the Machine Learning Specialization series. Whoever read my previous course comments will find this may sound repeating, but once again I need to praise Ng's way to explain the topic, which made clear some details - particularly on the decision trees videos - that were not so clear to me, even after a couple of MBA classes about the topic. I do recommend this course.

创建者 Will H

Jun 19, 2022

An excellent update to the previous Machine Learning course. Goes into excellent detail about each algorithm and the practical notebooks are useful and easy to follow.

创建者 Davi M

Jul 1, 2022

I really enjoy doing this course. Thanks!

创建者 RyounHeo

Jul 1, 2022

The best machine learning course!!!

创建者 Hritik A

Jul 1, 2022

Watched till week2. Great Content

创建者 Fernando A

Jul 1, 2022

Excellent course!

创建者 Rajeev R

Jun 20, 2022

Best course

创建者 Raktim M

Jun 28, 2022

The content is excellent but some more emphasis must be given on the discussion of the codes in the Jupyter Notebooks otherwise it'll become less appealing to the once who don't have a good grasp over Python.