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Art and Science of Machine Learning

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Art and Science of Machine Learning
Online Course Highlights
  • Google Cloud via Coursera
  • Learn for FREE, Ugpradable
  • 19 hours of effort required
  • 20,944+ students enrolled!
  • ★★★★★ (1,346 Ratings)

This is a course that helps individuals to gain important skills. The abilities that you will acquire through this course are ML instinct, practical insight, and experimentation expected to finely tune and streamline ML models for the best exhibition. This course will help you gain the techniques to generalize your model using Regularization techniques.

Online Course Effectiveness Score by TakeThisCourse.net:

Content Engagement Practice Career Benefit
Good
★★★★☆
Fair
★★★☆☆
Good
★★★★☆
Good
★★★★☆

What will you learn through this Course?

With the help of this course, you will be able to tune the batch size and learning rate for better model performance. Moreover, you will know the correct way to optimize an ML model.

Feedbacks

Now we will be looking at some of the reviews of this course. It will help you to get to know more about the course.

Positive Feedback

  • The best course ever to provide you a glimpse of the full ML land. What’s ML? Why ML? This course is the most significant way to do ML for real world business problems. I have learned everything from developing models to model evaluation, serving in production with all scaling tips and techniques with the essence of Cloud. This is the most effective course for ML practitioners out there. I have actually gained a lot, and what other place to find out from other than those who are literally doing it for the foremost popular products used across the online and mobile world (Soham M, ★★★★★).
  • I have learned plenty of things about Machine Learning from this course. I would like to thanks the Google Cloud team for providing us with this awesome course. Actually, there are many Machine Learning courses or articles out there, but in my opinion, nobody tells us, what’s the simplest thanks to doing one thing. This course has helped me to know why I’ll use this step certainly things. If anyone tells but he couldn’t show the correct visualization, it’s the proper thanks for doing so. Now I will be able to participate in real-life projects to use what I learn (Badhan S, ★★★★★).
  • This course goes deeper into a way to improve your TensorFlow ML models’ performance without going deep into the models themselves. An honest intro to find out a way to systematically tune hyperparameters just like the learning rate or batch size. I feel like this course is also a way to start using regularization techniques and embeddings. Finally, the course also opens the door to practice estimators, keeping you interested in the subsequent courses (Patrick M A, ★★★★★).

Negative Feedback

  • Too shallow to really be useful. I feel if anything it gives you, it is a plan of what is possible and roughly the areas you ought to explore. I did find a lot through the course. I will not be able to follow the course knowledge practically (Laimonas S, ★★★☆☆).
  • Good course, but I could not recover from the Estimator API. IMHO it’s too complicated compared to Kera’s and that I just couldn’t force myself to worry about it (Dimitry I, ★★★☆☆).

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