It was like opening up a door to a whole new world. I have discovered new tools that I will thoroughly enjoy to use for the exploration of data and for predictions. Thanks Team Coursera !

Some of the terms used here vary from the terms used in the industry. For example recall, precision etc. Overall this is a very good course with provides basics of machine learning.

Jeff Leek is a great professor .The delivery of the course material is very clear and covers a lot of predictive methods by using mainly R's caret package. Recommended for sure.

Very good course. Clear explanations and examples give a good overview of the foundations of Machine Learning. After this course the student can build Machine Learning models.

The practical machine learning course is a booster for the data science aspirant.The concept taught by the Prof Jeff Leek is easily understandable. Thank you so much Sir.

Practical Machine learning helped me to achieve my personal goals. Algorithm of prediction became clear, that gives the understanding of main point of the data science.

A great course that really helps demystify what machine learning is and how anyone can use it to build prediction models and start to answer tough questions using data.

Great primer for machine learning with ample additional resources for those who are interested. I feel this course gave me a solid basis to delve deeper into the topic.

I want to learn ML in R so I go straight to this course without taking any other course in this specialization, and it doesn't disappoint me. Thanks for a great course!

This is by far the most enlightening class in the whole specialization. I really got a good handle about how to build a predictive model and apply it to real datasets.

This course was really informative and extremely efficient by letting you know just the few basics needed to build some quite advanced models such as random forest..

This was my favorite class of the specialization. It was taught very well, and I felt like everything I learned in the previous classes were finally coming together.

Excelent course, it's a little bit short considering the breadth of the topic, but covers the most important algorithms and never abandon it's focus on methodology.

The course gets you deep into ML very quickly ...but I think that's enough to get someone introduced to machine learning. The recommended book a great accompaniment

This is by far the best machine learning course I took. This course is more hands-on "Machine Learning" kind rather than providing just a bunch of videos to watch.

It very well done, good pace, and gives you real and concrete elements and examples to build a fully functional machine learning algorithm! i recommend this course

Nice introduction to machine learning in R. It is rather basic level, so it not for people that already know some basics related to regression and classification.

A nice overview of the most popular Machine Learning algorithms. Also very thorough, given the limited amount of time. I recommend anyone interested to take it!

The best course of the specialization along with the statistical inference one - the final assignment is very fun to do, pretty much like a Kaggle competition.

Great introduction to Machine Learning in R. Concepts explained very clearly and project gave opportunity to test out the concepts introduced to real data.

You will learn how to use the caret package and learn how to implement ML algorithms. If you want the theory behind it, you need to go to other courses

Excellent course. I feel like i know so much already even though we scratched the tip of the iceberg. Will definitely enroll in more advanced courses.

Great course, thank you. I was able to use what I have learned from the previous 7 courses and see them on in action through this course. Thank you :)

The course gave enough of an introduction to allow me to pursue many of the topics on my own. The course does require a fair amount of outside study.

Very fine course in machine learning where the focus is more on the use of ML rather on the theory behind it i.e. the course title fits its contents.