- University of Washington via Coursera
- 18 hours of effort required
- 332,764+ already enrolled!
- ★★★★★ (12,216 Reviews)
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.
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.
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
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.
- 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, ★★★★★).
- 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, ★★☆☆☆).
Some of the related courses to the Machine Learning Course are:
- Stanford University via Coursera
- 54 hours of effort required
- 3,301,832+ already enrolled!
- ★★★★★ (139,119 Ratings)
- IBM via Coursera
- 20 hours of effort required
- 136,459+ already enrolled!
- ★★★★★ (8,672 Ratings)
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.