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Who is the target audience?

• Beginners who want to learn to use Artificial Intelligence
• Prior coding experience is helpful
• Topics involve intermediate math, so familiarity with university-level math is very helpful

What will I 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 app with Python that uses data to predict the stock market

What are the requirements?

• PyCharm Community Edition 2017.2.3.

Do you want to predict the stock market using artificial intelligence? Join us in this course for beginners in automating tasks.

In this course, you learn how to code in Python, calculate linear regression with TensorFlow, and make a stock market prediction app. We interweave theory with practical examples so that you learn by doing.

AI is a 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.

Our Promise to You

By the end of this course, you will have learned how to build a stock market prediction app with artificial intelligence.

30 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

### Course Curriculum

 Section 1 - Introduction Course Trailer 00:00:00 What Is Python Artificial Intelligence? 00:00:00 Section 2 - Python Basics Installing Python And PyCharm 00:00:00 How To Use PyCharm 00:00:00 Intro And Variables 00:00:00 Multi-Value Variables 00:00:00 Control Flow 00:00:00 Functions 00:00:00 Classes And Wrap-up 00:00:00 Source Files 00:00:00 Section 3 - TensorFlow Basics Installing TensorFlow 00:00:00 Intro And Setup 00:00:00 What Is TensorFlow? 00:00:00 Constant And Operation Nodes 00:00:00 Placeholder Nodes 00:00:00 Variable Nodes 00:00:00 How To Create A Linear Regression Model 00:00:00 Building A Linear Regression Model 00:00:00 Source Files 00:00:00 Section 4 - Stock Market Prediction: Project Introduction 00:00:00 Project Overview 00:00:00 Understanding Datasets 00:00:00 Importing And Formatting Data We Want 00:00:00 Calculating Price Differences 00:00:00 Building A Computational Graph 00:00:00 Training A Model 00:00:00 Testing Model Accuracy 00:00:00 Summary And Outro 00:00:00 Source Files 00:00:00