This course is designed for those interested to have a better understanding on how artificial intelligence works, by building a stock market prediction app. Read more.
John Bura has been programming games since 1997 and teaching since 2002. John is the owner of the game development studio Mammoth Interactive. This company produces XBOX 360, iPhone, iPad, Android, HTML 5, ad-games and more. Mammoth Interactive recently sold a game to Nickelodeon! John has been contracted by many different companies to provide game design, audio, programming, level design and project management. To this day, John has 40 commercial games that he has contributed to. Sev
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Who this course is for:
- 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 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 app with Python that uses data to predict the stock market
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.Â
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.
10 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.
Get started today and learn more about building a stock market prediction app.
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 | ||
About This Course
Who this course is for:
- 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 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 app with Python that uses data to predict the stock market
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.Â
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.
10 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.
Get started today and learn more about building a stock market prediction app.
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 | ||
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