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Real-World Machine Learning Projects Using TensorFlow [Video]

This is the code repository for Real-World Machine Learning Projects Using TensorFlow [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

Machine learning algorithms and research are mushrooming due to their accuracy at solving problems. This course walks you through developing real-world projects using TensorFlow in your ML projects.

The initial project will deal with assessing the viability of expanding your Restaurant business using a single variable linear regression. You will use Linear Regression with multiple variables with an example involving buying and selling a property at the best prices and use a dataset containing 11 features to deal with it. Next, you will create an algorithm to detect anomalous behavior in server computers using Gaussian methods. Finally, you'll design and build a convolutional Neural Networks model on a Traffic Signal Classifier from scratch.

By the end of this course you will be using TensorFlow in real-world scenarios, and you'll be confident enough to use ML Algorithms to build your own projects.

What You Will Learn

  • Explore topics such as classification, clustering, regression, and anomaly detection to build efficient ML models using TensorFlow 
  • Use multiple ML algorithms and explore how algorithms are used to solve problems by using them effectively
  • Implement the most widely used machine learning algorithms and learn to design and build a convolutional neural network from scratch 
  • Build real-world projects with predictive models, classification, anomaly detection algorithms, and Support Vector Machines. 
  • Create data models and understand how they work by using different types of dataset. 
  • Compare ML algorithms, and pick the best one for specific tasks

Instructions and Navigation

Assumed Knowledge

To fully benefit from the coverage included in this course, you will need:
Prior familiarity with TensorFlow and Machine Learning algorithms

Technical Requirements

This course has the following software requirements:

the Data_set's used

Minimum Hardware Requirements

For successful completion of this course, students will require the computer systems with at least the following:

● OS: Windows

● Processor: intel

● Memory: 4 GRAM

● Storage: 30 GB

Recommended Hardware Requirements

For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:

● OS: Windows

● Processor: intel CPU + advanced GPU

● Memory: 4 GRAM

● Storage: 60 GB

Software Requirements

● Operating system: Windows

● Browser: Firefox

● Anaconda , Pycharm

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