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Отзывы учащихся о курсе Launching into Machine Learning от партнера Google Cloud

Оценки: 3,100
Рецензии: 349

О курсе

Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation. Course Objectives: Identify why deep learning is currently popular Optimize and evaluate models using loss functions and performance metrics Mitigate common problems that arise in machine learning Create repeatable and scalable training, evaluation, and test datasets...

Лучшие рецензии


Dec 02, 2018

This is an awesome module. It will open up so much inside story of ML process which is core of the topic with such a simplicity. It greatly increases my interest into this topic and this course :)


Aug 04, 2018

Good course, covering all the basics about machine learning and most importantly, everything that surrounds an ml project and you need to take into account to make your ml project successful.

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1–25 из 347 отзывов о курсе Launching into Machine Learning

автор: Raghuram N

Apr 27, 2019

Great course. Gradient descent and loss function concepts were explained well.

автор: Dirk K

Aug 24, 2018

The videos are ok, the "Labs" are really bad. You just follow instructions with code to copy into the notebook. Of course, you can play a bit with the code, but you don't really learn how to do it yourself when the correct answer is already filled in. Would not recommend.

автор: Apoorv R

Nov 27, 2018

hands on labs was amazing

автор: Pawan K T

Dec 02, 2018

This is an awesome module. It will open up so much inside story of ML process which is core of the topic with such a simplicity. It greatly increases my interest into this topic and this course :)

автор: pankaj b g

Dec 03, 2018

Good learning path

автор: Raja R G

Dec 05, 2018


автор: Hussian A A

Dec 27, 2018

I loved TensorFlow Playgrounds. It made so many concepts visible. I have more intuition into how the number of layers, input features and number of neutrons affect what the model can learn. This is not my first Machine Learning course, and it is helping me fill out many gaps I have in my understanding.

автор: Alan I S R

Jan 15, 2019

Excelente curso, muchas gracias!

автор: rohit k s

Dec 31, 2018

Another great learning experience and has given me the confidence to keep moving forward.

автор: Gregory R G J

Jan 24, 2019

Thumbs Up!

автор: Sujeethan V

Mar 20, 2019

Decent, Labs took a hit on quality. :S

автор: Tan C M

Mar 24, 2019

Mainly covering the principles of data selection and bucketing for training, validation and test sets.

автор: Kiana T

Apr 09, 2019

My favourite course in the specialisation. I think it's a great idea to use historic time-frame to explain the advances in ML and why there is so much hype around deep learning.

автор: Akash G

Apr 10, 2019


автор: Mohammad S

Apr 10, 2019

It was great.......... Thanks Google :)

автор: 오승주

Apr 11, 2019


автор: Bill F

Apr 12, 2019

This course provides a solid foundation for data science.

автор: Dong H S

Apr 14, 2019

I can start to handle some part of ML after listening this course.

Thank you.

автор: 이동규

Apr 14, 2019


автор: Arif N

Mar 07, 2019

Thank you for such great knowledge sharing. I have really enjoyed the course and have learned a lot from it. The way the speakers explain each and every tiny detail is exceptional. This course make me a step closer to my goals and will help me in my career building as a Machine Learning Engineer.

автор: Shayne C

Dec 09, 2018

Super fun course.


Feb 05, 2019

5 starz - very good info and pointful.

автор: shabeer m

Feb 05, 2019

fanstatic course

автор: Kaustubh M H

Feb 13, 2019

This course gave me a good overview of how to work with GCP for ML and also helped in covering a bit of knowledge gaps that I had when I learnt things on my own.