Chevron Left
Retour à Launching into Machine Learning

Avis et commentaires pour l'étudiant pour Launching into Machine Learning par Google Cloud

4.6
3,016 notes
340 avis

À propos du cours

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 COMPLETION CHALLENGE Complete any GCP specialization from November 5 - November 30, 2019 for an opportunity to receive a GCP t-shirt (while supplies last). Check Discussion Forums for details....

Meilleurs avis

PT

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 :)

PA

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.

Filtrer par :

51 - 75 sur 338 Examens pour Launching into Machine Learning

par Zeev K

Oct 04, 2018

great value, thanks

par Fernando N

Oct 23, 2018

GREAT COURSE! EVAN JONES IS AMAZING!

par Mohan N

Oct 23, 2018

Practical ML

par kookjin k

Oct 27, 2018

fasdf

par Pavan

Oct 30, 2018

great way to understand about loss functions,performance metrics,learning rate,etc.

par Arief R S

Nov 03, 2018

Technical but not fot beginner

par Shuo D

Aug 28, 2018

Lab and the hand-on session were very useful! Also, the tip of using mod + rand() helps me to solve my own problem! Good job!

par Ezequiel A

Jul 25, 2018

Amazing Course!!!

par Meynardo J

Jul 28, 2018

Great intro to ML as done by Google, and the related technologies like BigQuery.

par Suresh K

May 08, 2018

Excellent articulation of the material. Well thought of labs. Thank you for a great introduction

par Min L

May 30, 2018

The course give a good introduction of machine learning and hands on exercise. It is practical and efficient.

par Sreenivasulu B

May 11, 2018

Nice introduction course!

par Danny L H S

Jul 07, 2018

great course! spliting data was very interesting

par Suresh

Jun 26, 2018

Its osm

par Srinivas V

Jun 24, 2018

Very good.

par Juan P D P

May 29, 2018

The teachers really try their best you understand the fundamentals giving you examples and showing in an easy way how you can do it.

par Jun W

May 25, 2018

An excellent course, thank you Googlers. My favorite part is the intuition about ML.

par Carlos V

Jun 05, 2018

An excellent introduction to Machine Learning, I appreciated the explanations around the importance of having proper training, validation and testing set to build robust models, I loved the introduction to Big Query and the value of cleaning the datasets, plus all the explanations around Classification Models,Regression Models and Gradient Descent.

Thanks

par Jorge S A M B

Jun 15, 2018

Very good

par Michael F

Apr 11, 2018

awesome

par Emre S

Apr 29, 2018

The technical knowledge is introduced very progressively. You understand the historic evolution and practical usage of models. Great content!

par Oleg O

Jun 11, 2018

Very good intro into understanding of how models work and how to get data ready for processing

par Agata S

Jun 21, 2018

Very practical, pragmatic and to the point. The labs are great! The history of ML and the section on Generalization are my favorite because instructors gave detailed explanations and precise instructions.

par Atichat P

Jun 01, 2018

Good

par Akash P

Apr 17, 2018

Good information and content for getting stated with machine learning.