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Hands-On Neural Networks​: Build Machine Learning Models​

 Hands-On Neural Networks​: Build Machine Learning Models​
Udemy course  Hands-On Neural Networks​: Build Machine Learning Models​
Build Machine Learning Models . Udemy – A business is a method of form or to obtain money in Exchange for products, services, or any activity that you wish to develop. Like this course

What you'll 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.
  • Make an app with Python that uses data to predict the stock market.
 Description

Build 2 complete projects start to finish -- with each step explained thoroughly by instructor Nimish Narang from Mammoth Interactive.

Hands-On Neural Networks: Build Machine Learning Models was funded by a #1 project on Kickstarter

Nimish is our cross-platform developer and has created over 20 other courses specializing in machine learning, Java, Android, SpriteKit, iOS and Core Image for Mammoth Interactive. When he's not developing, Nimish likes to play guitar, go to the gym and laze around at the beach.

Project #1 -- Learn to construct a model for credit card fraud detection. Our model will take in a list of transactions, some fraudulent and some legitimate. It will output the percentage at which it can calculate fraudulence and legitimacy, how accurate it is. We will also modify the model so that it output whether a specific transaction is fraudulent or legitimate if we pass them in one by one.

We will explore a dataset so that you fully understand it, and we will work on it. It's actually pretty hard to find a dataset of fraudulent/legitimate credit card transactions, but we at Mammoth Interactive have found everything for you and curated a step by step curriculum so that you can build alongside us.

We will manipulate the dataset so that it will be easy to feed into our model. We will build a computational graph with nodes and functions to run input through the mini neural network.
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