Udemy - Detect Fraud and Predict the Stock Market with TensorFlow
- CategoryOther
- TypeTutorials
- LanguageEnglish
- Total size1 GB
- Uploaded Bytutsgalaxy
- Downloads258
- Last checkedAug. 23rd '19
- Date uploadedAug. 22nd '19
- Seeders 30
- Leechers7
Description
+FREE gift! Learn to use Python Artificial Intelligence for data science. Learn predictive modeling & linear regression!
This course was funded by a #1 project on Kickstarter
Do you want to learn how to use Artificial Intelligence (AI) for automation? Join us in this course for beginners to automating tasks.
You will learn how to code in Python, calculate linear regression with TensorFlow, analyze credit card fraud and make a stock market prediction app.
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.
AI like TensorFlow is great for automated tasks including facial recognition. One farmer used the machine model to pick cucumbers!
Included in this course is material for beginners to get comfortable with the interfaces. Please note that we reuse this content in similar courses because it is introductory material. You can find some material in this course in the following related courses:
Fraud Detection with Python, TensorFlow & Linear Regression
Make an Artificial Intelligence Stock Market Prediction App
The Complete Unity and Artificial Intelligence Masterclass
The Ultimate Unity Games & Python Artificial Intelligence
Who this course is for:
Beginners who want to learn to use Artificial Intelligence.
Prior coding experience is helpful. For an in-depth intro to Python, search for our Ultimate Python Beginner Course.
Topics involve intermediate math, so familiarity with university-level math is very helpful.
Requirements
Please download PyCharm Community Edition 2017.2.3.
Last updated 1/2019
Files:
Detect Fraud and Predict the Stock Market with TensorFlow- Detect Fraud and Predict the Stock Market with TensorFlow.zip (1.0 GB)
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