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

4.9
stars
8,549 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

AK

Jul 11, 2024

what amazing course, I had never thought that i could understand these complex ml algorithm but this course not only made me understand them also taught me create these models from scratch🤯

AA

Dec 29, 2024

The course provides an excellent introduction to widely used machine learning concepts, including Neural Networks. While the material can be challenging, it is presented in a digestible manner.

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1151 - 1175 of 1,282 Reviews for Advanced Learning Algorithms

By HANIUM M J (

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Aug 20, 2024

best

By RATHOD Y A

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Aug 4, 2024

nice

By Jeevotthama S K .

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Jul 29, 2024

BEST

By Dinesh M

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Jul 28, 2024

Good

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Nov 16, 2023

Good

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Nov 11, 2023

nice

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Oct 14, 2023

good

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Oct 12, 2023

good

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Oct 8, 2023

Joss

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Sep 19, 2023

good

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Mar 22, 2023

cool

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Feb 17, 2023

Good

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Feb 4, 2023

nice

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Jan 15, 2023

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By do v

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Mar 1, 2025

Ok

By Lee_Infinitee

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Feb 2, 2025

:)

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Mar 5, 2024

:)

By Jaber

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Sep 3, 2022

<3

By Naveen R

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Aug 30, 2025

s

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Dec 27, 2024

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By Bhavesh P

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Jul 9, 2023

By Serge B

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Nov 30, 2022

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By Will S

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Jan 3, 2023

Really good conceptual teaching of ANNs and decision trees, but it's a little lacking in the Python implementation. It teaches you how to program an ANN with any number of layers/neurons, but there is no mention of finding the "optimal" number of each. The last week on decision trees and ensemble models feels rushed as there is only one lab and required assignment, so it completely misses the Python implementation of XGBoost. However, it teaches the essential functions in each library, so one can easily continue his or her learning with Kaggle competitions and Stack Overflow. In the end, it's meant to introduce working professionals to the most common ML models in the world today, and it does that very well, but not much more.