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Real-World Machine Learning Projects Using TensorFlow

Real-World Machine Learning Projects Using TensorFlow
$125 | Duration: 2h 31m | Video: h264, 1920x1080 | Audio: AAC, 48kHz, 2 Ch | 488 MB
Genre: eLearning | Language: English | November 30, 2018

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.

The code bundle for this video course is available at -
Style and Approach
The course first defines a problem and then it gives you its solution along with the steps to solve it practically by using Python with TensorFlow. You will be working and building examples from scratch, starting with simple problems and progressing to complicated ones.

Table of Contents

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
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
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