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Build and train a data model to recognize objects in images! (Udemy.com)

Make an image recognition model with TensorFlow & Python predictive modeling, regression analysis & machine learning!

Created by: Mammoth Interactive

Last updated January 2019

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What you will learn

  • Learn how to code in Python, a popular coding language used for websites like YouTube and Instagram.
  • Learn TensorFlow and how to build models of linear regression.
  • Make an image recognition model with CIFAR.

How it compares

Against the other 461 rated Udemy courses in Data Science on CourseDuck:

  • Rating4.2 out of 5higher than 9% of them
  • Reviews207#342 of 462 by number of reviews
  • Length8.4 hoursshorter than 50% of them
  • Last updatedJanuary 2019earlier than 94% of them

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Course Description

"Well done!!!!!! I found it the BEST source for me out of many to learn how to implement AI project due the facts it starts from the very basics of Python and TensorFlow and assumes no prior knowledge (or almost no prior knowledge) which should not be taken for granted since other courses do so. The instructor is wonderful and explains all the concepts wonderfully! Thank you so much! helped me a lot!"

"Very easy to understand. Loving it so far!" - Arthur G.

This course was funded by a wildly successful Kickstarter.

Let's learn how to perform automated image recognition! In this course, you learn how to code in Python, calculate linear regression with TensorFlow, and perform CIFAR 10 image data and recognition. We interweave theory with practical examples so that you learn by doing.

AI is code that mimics certain tasks. You can use AI to predict trends like the stock market. Automating tasks has exploded in popularity since TensorFlow became available to the public (like you and me!) AI like TensorFlow is great for automated tasks including facial recognition. One farmer used the machine model to pick cucumbers! 

Join Mammoth Interactive in this course, where we blend theoretical knowledge with hands-on coding projects to teach you everything you need to know as a beginner to image recognition.

Enroll today to join the Mammoth community!

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Instructor Details

Mammoth Interactive

Mammoth Interactive is a leading online course provider in everything from learning to code to becoming a YouTube star. Mammoth Interactive courses have been featured on Harvard’s edX, Business Insider and more.

Over 11 years, Mammoth Interactive has built a global student community with 3.3 million courses sold. Mammoth Interactive has released over 300 courses and 3,000 hours of video content.

Founder and CEO John Bura has been programming since 1997 and teaching since 2002. John has created top-selling applications for iOS, Xbox and more. John also runs SaaS company Devonian Apps, building efficiency-minded software for technology workers like you.

"I absolutely love this course. This is such a comprehensive course that was well worth the money I spent and a lot more. Will definitely be looking at more Mammoth Interactive courses when I finish this." – Student Matt W.

"Very good at explaining the basics then building to more complex features." – Student Kevin L.

Try a course today.


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Reviews

4.2

207 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Alex Jones on 7/22/2021

Really dislike the outro and intros. I'm watching these videos back to back so I don't need to see these every single time! Otherwise content is good.

By Gary Latham on 12/11/2020

Having lots of problems running the examples. Not sure why yet but all the examples from the TensorFlow site work without issue. Seems like I have a different TensorFlow install that is MUCH different.

By Tina on 8/10/2020

This is a great introductory course to get you started in computer vision with tensorflow.

By Keegan Bryan on 1/8/2020

Overall course was helpful for student with limited knowledge of this subject, however the course starts and ends with unnecessary animations. The course section arrangement could be better as the small project was done ahead of proper explanation on everything used in project thus since the lecturer already explained in the project section, other section might not be as useful. Note that this course require : Tensorflow < 2.0, if using tensorflow-gpu & has Nvidia Discrete GPU use Nvidia Cuda 10 and download separately Nvidia cuDNN. Luckily the lecturer remind to download any library mentioned in this course, otherwise require some Google search before able to complete the course.

By Gabriel Vince on 8/31/2018

This course is about implementing a simple image recognition project, I consider this course as a very good stepping stone into more complex and deeper subjects. Having some basics of Machine Learning prior this course is very useful

By James Wagstaff on 7/16/2018

Great course, but it looks like the instructor is not available, so any problems during the course you have to resolve yourself, or hope that somebody else is available to help

By Norfa Bagas on 3/17/2018

I am still basic about image recognition, and this course really helped me to learn this by making real project. Thank you

By Lior Orfelli on 2/7/2018

Well done!!!!!! I found it the BEST source for me out of many to learn how to implement AI project due the facts it starts from the very basics of Python and TensorFlow and assumes no prior knowledge (or almost no prior knowledge) which should not be taken for granted since other courses do so. The instructor is wonderful and explains all the concepts wonderfully! Thank you so much! helped me a lot!

By Mauri Carvalho on 12/19/2017

I cannot keep going with the course, see question: https://stackoverflow.com/questions/47992078/error-trying-to-split-image-colors-numpy-ndarray-object-has-no-attribute-mask

By Thomas Ott on 11/28/2017

The course has an optimal pace for beginners to Python and the topic as a whole. It definitely helps to do some research about the basics beforehand. But the tutor will make that clear on several occasions. One last thing: Thanks a lot for the work!

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Overall Score : 84 / 100