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Learner Reviews & Feedback for Advanced Learning Algorithms by DeepLearning.AI

4.9
stars
6,614 ratings

About the Course

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....

Top reviews

DG

Apr 14, 2023

Extremely educational with great examples. Helpful to know Python beforehand or the syntax will become a time sync, and understanding the mathematics as going through the class makes it a decent pace.

SL

Aug 27, 2022

After copleting the course I found all conceptual knowlegde for visualising and implementing the algorithm in my work. Before this course I was not using the full potential of the advanced algorithm

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626 - 650 of 1,020 Reviews for Advanced Learning Algorithms

By Đạt N

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Aug 1, 2022

content in this course is very good

By RyounHeo

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Jul 1, 2022

The best machine learning course!!!

By Yasin B K

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Aug 31, 2024

No reaction, just watch and learn.

By Meng G

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Feb 2, 2024

Great course! Learn a lot from it.

By sameh a

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Dec 25, 2023

Great course, I strongly recommend

By Young S S

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Dec 24, 2023

A great course! Thank you so much!

By Matheus B

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Sep 5, 2023

The Andrew methodology is awesome!

By Reimu K

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May 24, 2023

Please accept my heartfelt thanks.

By Gajanad P

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May 3, 2023

very helpful course for my studies

By Kimia M

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Oct 29, 2022

Simple, complete and comprehensive

By Ricardo G

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Mar 12, 2024

Very usefull and excited course!!

By Raghvendra M

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Oct 22, 2023

Awesome course, great instructor.

By Sanjib S

•

Sep 14, 2023

Exceptional and engaging learning

By duchoan m

•

Jul 1, 2023

Very good course! 5 stars from me

By Leith S

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Jun 24, 2023

Another great course from Andrew.

By MCMXCVII N

•

Mar 12, 2023

Perfect course for the beginners.

By Balaji

•

Oct 6, 2022

Got a clear understanding of NNs

By Alireza K

•

Aug 22, 2022

The best course I have ever taken

By Hritik A

•

Jul 3, 2022

Watched till week2. Great Content

By Manh T

•

Jul 18, 2024

Best Course about ML on Coursera

By Hasham A

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Dec 3, 2023

Fantastic and Amazing Experience

By Nilesh G

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Jun 29, 2023

Course was really very helpful !

By Marcos S

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May 16, 2023

Awesome course! Thanks everyone!

By Yuvraj J

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May 10, 2023

Best course for machine learning

By Justin H

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Feb 27, 2023

Andrew Ng. The man. The legend.