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Advanced Learning Algorithms(으)로 돌아가기

deeplearning.ai의 Advanced Learning Algorithms 학습자 리뷰 및 피드백

4.9
별점
48개의 평가
11개의 리뷰

강좌 소개

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....

최상위 리뷰

MM

2022년 6월 22일

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

2022년 6월 18일

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.

필터링 기준:

Advanced Learning Algorithms의 11개 리뷰 중 1~11

교육 기관: Mohamed N M

2022년 6월 23일

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.

교육 기관: Changlin F

2022년 6월 22일

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

교육 기관: Yuriy G

2022년 7월 1일

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.

교육 기관: rcotta

2022년 6월 28일

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.

교육 기관: Will H

2022년 6월 19일

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.

교육 기관: Davi M

2022년 7월 1일

I really enjoy doing this course. Thanks!

교육 기관: RyounHeo

2022년 7월 1일

The best machine learning course!!!

교육 기관: Hritik A

2022년 7월 3일

Watched till week2. Great Content

교육 기관: Fernando A

2022년 7월 1일

Excellent course!

교육 기관: Rajeev R

2022년 6월 20일

Best course

교육 기관: Raktim M

2022년 6월 28일

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.