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machine learning a-z review from PeakGet.com students
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We have collected all reviews from our website visitors about: Machine Learning A-Z and Deep Learning A-Z courses by Kirill Eremenko on Udemy portal. In fact our 2 team members: Juri Fab and Dorrow Green completed this courses as well and their opinion also included this this review.
These two courses are to be taken if you are a beginner in Machine Learning. If you really don’t want to deep dive into theoretical aspects of Machine Learning initially you should enroll this course. Instead you just want to know how to apply different ML techniques for solving real-life problems using standard Machine Learning frameworks provided in Python and R. The courses are pretty much hands-on. In that case you should be able to learn how to use R/Python for your ML problems.
In fact if you are good at programming in any language such as R, Python, Spark, Java you can pick up the theory from Andrew NG’s course. Then start implementing them in your favorite language on any freely available data. But if you are not so much familiar into programming then this course will definitely help you. Especially how to write clean code to develop Machine Learning pipeline in order to solve a real-life problem.
Under those circumstances: Go for Kiril Eremenko course if you want to get real experience in Machine Learning.
My personal opinion is to go for this course. Even if you are good at ML/DL as learning Python (Scikit learn/TensorFlow/Keras)/R frameworks does help a lot.
We would say it depends on the targeted applications. Deep learning is great for applications in which we know there exists a “low bias” model based on the data we have. In lots of cases, human brain is the low bias model 🙂 But in many other areas such as biological sciences and medicine, We doubt this is the case. Unfortunately, people start to overgeneralize things; and it seems that a lot of the hypes are now going to these areas.
For engineering applications in which the task is usually to automate certain things that humans can do easily, I think deep learning has a great potential and may still have a long way to go (speech recognition, self-driving cars, natural language processing, robotics, etc). In these applications, there are two key characteristics that make deep learning (or really any statistical models) POSSIBLE to work well. Thank you for reading machine learning a-z review.