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Learner Reviews & Feedback for Supervised Machine Learning: Regression and Classification by DeepLearning.AI

4.9
stars
27,433 ratings

About the Course

In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression 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 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

AD

Nov 24, 2022

Amazingly delivered course! Very impressed. The concepts are communicated very clearly and concisely, making the course content very accessible to those without a maths or computer science background.

FA

May 25, 2023

The course was extremely beginner friendly and easy to follow, loved the curriculum, learned a lot about various ML algorithms like linear, and logistic regression, and was a great overall experience.

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5151 - 5175 of 5,268 Reviews for Supervised Machine Learning: Regression and Classification

By luficerg f

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Jun 5, 2023

Very good

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Aug 16, 2022

Very Nice

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Aug 12, 2022

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Oct 26, 2023

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Mar 27, 2023

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Feb 3, 2023

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By Prajwal M

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Aug 22, 2024

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By Mohit G S 2

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Sep 25, 2023

Title: Frustrating Experience Due to Speaker and Technical Issues I enrolled in the course "Supervised Machine Learning: Regression and Classification" with high hopes, but I was left thoroughly disappointed. The most frustrating aspect of this course was the speaker's voice. At times, it was so annoying that it made it nearly impossible to concentrate on the content being presented. The speaker's tone and delivery lacked enthusiasm and clarity, making it difficult to stay engaged. To make matters worse, there were frequent microphone issues throughout the course. It felt like the words were either muffled or slurred, which not only made it hard to understand but also incredibly irritating to listen to. These technical problems seriously hindered my ability to learn and absorb the material effectively. While the course content itself was decent, the combination of the speaker's annoying voice and microphone problems made it a struggle to complete. I expected a much more professional and engaging learning experience, and sadly, this course fell far short of my expectations. Title: Valuable Content, Could Use Some Improvements I recently completed the course "Supervised Machine Learning: Regression and Classification," and I have mixed feelings about my overall experience. On the positive side, the course content was comprehensive and provided a solid foundation in supervised machine learning. The topics were well-structured, and I appreciated the depth of coverage on regression and classification techniques. The examples and exercises were helpful in reinforcing the concepts, and I did gain valuable insights into these subjects. However, there were some notable drawbacks. First and foremost, the speaker's voice occasionally made it challenging to stay engaged. There were moments when the tone and delivery were less than engaging, which detracted from the learning experience. Additionally, there were technical issues with the microphone that affected the audio quality. This made it difficult to understand certain parts of the course and was quite bothersome. In summary, the course content itself is valuable for anyone looking to learn about supervised machine learning, particularly regression and classification techniques. However, improvements in the speaker's delivery and addressing the technical issues with the microphone would greatly enhance the overall learning experience.