MK
5.0Reviewed Jul 20, 2019
A great course, really designed to understand the underlying core concepts of machine learning using real-life examples which takes you through all that with little to no programming skills required!
Machine Learning Foundations: A Case Study Approach
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.
Status: Predictive Modeling
Status: Machine Learning Algorithms
Course18 hours
Featured reviews
RM
4.0Reviewed Feb 2, 2022
I was very disappointed with the exclusion of the final courses and the capstone project. The most interesting part of specialization no longer exists and no plausible justification has been given.
RH
5.0Reviewed Jun 8, 2017
I felt this course did a good job introducing the student to Machine Learning. The examples and hands on assignments brought the concepts home. I was able to use the knowledge immediately at work.
FA
5.0Reviewed Dec 26, 2022
Amazing course, lots of great ideas and amazing instructors, i really enjoyed it and looking forward to see what's coming next in the specialization. Also i am really greatfull for this information
GG
5.0Reviewed Jun 4, 2017
This course is very helpful for people who are novice in machine learning. The course uses Graphlab Create which is different from scikit or R-libraries, but the tool(Graphlab) is excellent to use.
AA
5.0Reviewed Nov 13, 2016
Really liked the course and the teachers. Would have preferred more detail on the quizzes so I didn't feel as lost as I did some of the times while trying to piece together what a question meant.
DP
5.0Reviewed Feb 14, 2016
With a funny and welcoming look and feel, this course introduces machine learning through a hands-on approach, that enables the student to properly understand what ML is all about. Very nicely done!
DM
4.0Reviewed Apr 1, 2017
The Course was very neatly presented, although we used lots of predefined functions to work around Machine Learning Algorithms it was good to know about the concepts that was thought extremely well.
CL
5.0Reviewed Oct 7, 2019
This was good introductory course with challenging programming assignments that expanded and grounded the lecture materials. The forums also proved great support when needed, overall very satisfied.
GS
5.0Reviewed Nov 7, 2016
One of the best courses available online. Actually got to know how to apply theoretical knowledge in designing systems. You people are the best and made concepts and things really easy. Hats off!!
KK
5.0Reviewed Feb 10, 2019
The course module is very clear and very useful for me to understand the ML concepts.Really excited about more features in the C_Stone project where i think we can do something for my organisation.
SZ
5.0Reviewed Dec 19, 2016
Great course! Emily and Carlos teach this class in a very interest way. They try to let student understand machine learning by some case study. That worked well on me. I like this course very much.