TensorFlow Mastery: Constructing Dynamic Machine Learning Models
Build a full portfolio with practical machine learning projects.
Take your skills to the next level by building a huge range of models.
- Build regression and classification models
- Build artificial intelligence search algorithms
Build a full portfolio with practical machine learning projects.
- Use Tensorflow 2.0 and Keras to build fun beginner projects.
- Classify images, species of plants and more.
Your Instructor
Alexandra Kropf is Mammoth Interactive's CLO and a software developer with extensive experience in full-stack web development, app development and game development. She has helped produce courses for Mammoth Interactive since 2016, including the Coding Interview series in Java, JavaScript, C++, C#, Python and Swift.
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 12 years, Mammoth Interactive has built a global student community with 4 million courses sold. Mammoth Interactive has released over 350 courses and 3,500 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.
Course Curriculum
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Start00. Course Intro (6:10)
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Start01. Intro to Tensorflow (5:32)
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Start02. Installing Tensorflow (3:52)
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Start03. Intro to Linear Regression (9:26)
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Start04. Linear Regression Model - Creating Dataset (5:49)
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Start05. Linear Regression Model - Building the Model (7:22)
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Start06. Linear Regression Model - Creating a Loss Function (5:57)
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Start07. Linear Regression Model - Training the Model (12:42)
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Start08. Linear Regression Model - Testing the Model (5:22)
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Start09. Summary and Outro (2:55)
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StartSource Files
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Start00. Course Intro (6:57)
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Start01. Intro To Image Recognition (14:07)
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Start02. Intro To Mnist (4:42)
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Start03. Building A CNN Part 1 - Obtaining Data (15:40)
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Start04. Building A CNN Part 2 - Building The Model (10:14)
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Start05. Building A CNN Part 3 - Adding Loss And Optimizer Functions (4:57)
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Start06. Building A CNN Part 4 - Train And Test Functions (10:58)
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Start07. Building A CNN Part 5 - Train And Test The Model (9:17)
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Start08. Mnist Image Recognition With Keras Sequential Model (13:23)
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Start09. Summary And Outro (3:40)
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StartSource Files