Real-World Machine Learning Projects Using TensorFlow

Real-World Machine Learning Projects Using TensorFlow

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 31m | 503 MB

Build real-world projects and train them using machine learning algorithms with TensorFlow

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 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.

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
Table of Contents

Getting Started with TensorFlow
1 The Course Overview
2 Installing and Preparing the Environment
3 Installing TensorFlow
4 Warming Up Examples

Linear Regression with One Variable
5 What Is Machine Learning
6 Model Representation and Gradient Descent
7 Problem Statement and Solution

Linear Regression with Multi Variable
8 Model Representation
9 Problem Statement
10 Problem Solution

Anomaly Detection Algorithm
11 What Is Anomaly Detection
12 Server Computer’s Behavior

Traffic Sign Classifier
13 Introduction to Traffic Sign Classifier
14 Implementing Traffic Sign Classifier