Course Highlights

University of Washington via Coursera
18 hours of effort required
364,929+ already enrolled!
★★★★★ (13,104 Reviews)

Enroll Now for Free

Have you ever wondered what having data means? What type of information can you get through data? In this specialization, you will be understanding the core ways in which machine learning can improve your business. You will have more information on how to converse with specialists. You will able to give a communion regarding regression and classification to deep learning and recommender systems.

By utilizing a series of practical case-studies, you will gain a thorough, hands-on experience with machine learning. You will understand everything related to machine learning. You will be preparing yourself to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images.

TTC Course Analysis:

Following are the results of comprehensive analysis of “Machine Learning Foundations: A Case Study Approach” online course by our team of experts.

TTC Rating
3,050 Reviews

4.4

TakeThisCourse Sentiment Analysis Results:

In order to facilitate our learners with real user experience, we performed sentiment analysis and text mining techniques that generates following results:

TTC analyzed a total of 3,050 reviews for this online course.
The analysis indicates that around 87.6% reviews were positive while around 12.4% of reviews had negative sentiment.
University of Washington’s online course received a total score of 4.4 out of 5, based on user opinions related to 4 effectiveness factors including content, engagement, quality practice and career benefit.

Review for Machine Learning Foundations A Case Study Approach

TTC Sentiment Analysis based on Learner Reviews

TTC Course Effectiveness:

Online Course Effectiveness Score
Content	Engagement	Practice	Career Benefit
Excellent ★★★★★	Excellent ★★★★★	Good ★★★★☆	Good ★★★★☆

Based on the ratings and feedback from actual users, we conclude the following good aspects of this course:

The content of the course is very informative and inspirational.
The lectures are very relevant and everything is explained clearly by the instructors.
Offers quizzes and assignments with just the right length.
Helps to improve existing skills and develop more in the way.

Pros and Cons:

Pros:

Beginner-friendly content.
Challenging assignments to offer.
Great peer support through the forum.
Teaches underlying machine learning core concepts.

Cons:

Some material is outdated.

What will you learn through this Machine Learning Foundations Course?

By the end of the course, you will be able to identify potential applications of machine learning in practice and select the appropriate machine learning task for a potential application. Furthermore, you will have enough knowledge to describe the core differences in analyses enabled by regression, classification, and clustering.

In addition, you will be able to apply regression, classification, clustering, retrieval, recommender systems, and deep learning. You will acquire the skills to represent your data as features to serve as input to machine learning models. You will learn to assess the model quality in terms of relevant error metrics for each task and utilize a dataset to fit a model to analyze new data. After the successful completion of this course, you will be able to build an end-to-end application that uses machine learning at its core and be able to implement these techniques in Python.

You can also find out 10 Best Coursera Machine Learning Courses 2021.

What are the skills that you will gain?

Through this course, you will be able to gain the following skills:

Python Programming
Machine Learning Concepts
Machine Learning
Deep Learning

What People Are Saying About this Professional Certificate:

Let’s have a look at a review for this Machine Learning Foundations course. We will be looking at both positive and negative feedback. So that you can have a better idea if this course is worth it or not.

Positive feedback:

A great course. Very well designed to know the underlying core ideas of machine learning using real-life examples that take you thru all that with very little to no programming skills required ( Muhammad W K, ★★★★★).
Great course! Emily and Carlos teach this course in an exceedingly interesting way. They fight to let students perceive machine learning by some case studies. That worked well on me. I prefer this course very much (Sam Z, ★★★★★).
Very good overview of ML. The GraphLab API wasn’t that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall, I enjoyed it very much and also learned very much (Brett L, ★★★★★).
The course was simple and delivered by all the trainers with the assistance of a case study and nice examples. The forums and discussions were very helpful and useful whereas doing the assignments (Pooja M, ★★★★★).
This course is pretty good for beginners. All domains are explained briefly as an introduction. The best part about this course is the very good hands-on sessions which are really helpful to understand concepts. The course is not very detailed, but it’s very good to start with. Looking forward to quality courses ahead in this specialization (Rohit, ★★★★★).

Negative feedback:

Excellent Theory. Extremely clear explanations with straightforward yet powerful examples. Sadly, the practical half isn’t nearly as good. Mainly due to the tool used. If this was enforced in Scikit-Learn, the course would be glorious overall (Ezequiel P, ★★★☆☆).
The course seems outdated in many aspects, the support isn’t available to clarify doubts, and the documentation isn’t updated either. Moreover, the software support has ended (Ayush G, ★★★★★).
I think this course is out-of-date as they’re using python 2 additionally the platform, they use for machine learning is solely supported by python 2. because of these limitations, I too was unable to continue this course. as a result of when you have to figure with new libraries you have to uninstall python 2 and install python 3 (Waqar H, ★★☆☆☆).

Is this Course worth taking?

We believe the course has introduced learners to Machine Learning through a hands-on approach and allows them to understand all about Machine Learning conveniently. The instructor played a vital part in identifying the potential applications of Machine Learning in practice. Given what the course has to offer, it is safe to say this course is worth taking.

Related Courses

Some of the related courses to the Machine Learning Course are:

Machine Learning

- - Stanford University via Coursera
  - 54 hours of effort required
  - 3,301,832+ already enrolled!
  - ★★★★★ (139,119 Ratings)

Machine Learning with Python

- - IBM via Coursera
  - 20 hours of effort required
  - 136,459+ already enrolled!
  - ★★★★★ (8,672 Ratings)

Final Thoughts:

So, this was a Review for machine learning foundations: a case study approach. We hope that you gained a lot of information through this Machine Learning Foundations course review article. Stay safe and keep learning.

Enroll Now for Free

Your Feedback:

There are no reviews yet. Be the first one to write one.

Review for Machine Learning Foundations: A Case Study Approach