MA
5.0Reviewed Jan 27, 2025
I've really enjoyed learning about Machine Learning in such a guided way. It will continue to inspire me to learn more about AI. Thank you Andrew Ng, DeepLearning.AI, Standford ONLINE, and Coursera.
Supervised Machine Learning: Regression and Classification
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.
Status: Model Optimization
Status: Model Training
BeginnerCourse33 hours
Featured reviews
AA
4.0Reviewed Apr 29, 2023
Optional Lab lot more time than mentioned without prior experience of python and libraries used. Its estimated time should be change, it's a lot more than 1 hour. Video and exercises are very good.
WB
4.0Reviewed May 23, 2024
amazing course and super easy to follow. my only problem is that it doesn't delve too deeply into the math and science of things and focuses more on practical applications rather than how things work
SB
5.0Reviewed Nov 6, 2022
This course is a brief but thorough introduction. It has a good mixture of theory and practice.Andrew Ng explains every thing very good, understandable and in a fun way.I highly recommend this class!
ED
5.0Reviewed Apr 13, 2025
Loved Andrew Ng's videos and the hands on Jupyter notebook labs! My understanding of ML has significantly improved thanks to this course and going on to the next course to complete ML specialization!!
KB
5.0Reviewed Dec 6, 2024
I was completely new to Machine learning. It is an excellent course for complete beginners. Python codes are also downloadable and can be used for further reading another time. Very Very nice course!
AK
4.0Reviewed Jan 8, 2023
I learned a lot in this part and would like to continue further but one point that I would like to raise is that it would be better if you can tell us about the in general function that are used in ML
AM
5.0Reviewed Jul 16, 2022
It is the Best Course for Supervised Machine Learning!Andrew Ng Sir has been like always has such important & difficult concepts of Supervised ML with such ease and great examples, Just amazing!
NM
5.0Reviewed Jan 5, 2025
It is a good course if you have some background in ML or are looking into seeing if it's a field for you. I would suggest reading extra and building models to get maximum benefits from this course.
RG
5.0Reviewed Aug 30, 2024
The course was excellent, and I gained valuable knowledge throughout. I am also grateful for the financial aid, which allowed me to complete the program successfully. Thank you for this opportunity.
AD
5.0Reviewed Nov 23, 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.
AC
5.0Reviewed Oct 7, 2024
great course. Nice explanation even if you are not familiar with certain mathematical concepts Like Gradient. I would recommend having some mathematical base to ease the understanding of the course.