by Kaiser Hamid Rabbi | Released May 2018 | ISBN: 9781789136999

Transcend your machine learning experience by leveraging it with the cutting edge library - TensorFlowAbout This VideoThis course is an easy-to-understand guide to the complexities of Google's TensorFlow framework; you'll build seven amazing projects throughout the course.This course is designed to balance theory and practical implementation, with complete Jupyter Notebook code guides and easy-reference practical examples.You'll also learn about the most cutting-edge machine-learning technology, deep learning, and we walk you through building an artificial neural network and a convolutional neural network step-by-step from scratch.In DetailWith this course you'll learn to take your data analysis and Python programming skills to the next level via Machine Learning using TensorFlow. This course focuses on key Machine Learning techniques and algorithms and you'll apply them practically using TensorFlow models in a hands-on approach. Each section covers a specific Machine Learning task and you will implement it on your system with TensorFlow models. For example, you will learn Logistic Regression and will then implement it with TensorFlow for your analysis tasks. You'll implement techniques such as Classification and Clustering effectively using TensorFlow. Similarly, this course takes you through different ML tasks/algorithms and teaches you to implement them in your applications/systems.All the code and supporting files for this course are available on: https://github.com/PacktPublishing/Hands-on-Machine-Learning-with-TensorFlow Show and hide more

1. Chapter 1 : Getting Started with TensorFlow
   • The Course Overview 00:04:53
   • Installing TensorFlow Environment 00:07:49
   • TensorFlow Basic Syntax 00:09:55
   • TensorFlow Graphs 00:05:57
   • Variables and Placeholders 00:06:27
2. Chapter 2 : Apply Regression Techniques in TensorFlow
   • What is Machine Learning? 00:06:35
   • Regression from Scratch for 1 Million Data Points – Part 1 00:14:23
   • Regression from Scratch for 1 Million Data Points – Part 2 00:10:12
   • Housing Price Prediction Model with Estimator API 00:09:02
3. Chapter 3 : Implementing Classification Techniques Using TensorFlow
   • Performing Classification Techniques on Pima Indians Diabetes Dataset – Part 1 00:08:39
   • Performing Classification Techniques on Pima Indians Diabetes Dataset – Part 2 00:04:20
   • Performing Classification Techniques on Pima Indians Diabetes Dataset – Part 3 00:05:19
   • Predicting Class of Income on Census Data – Part 1 00:04:22
   • Predicting Class of Income on Census Data – Part 2 00:03:30
   • Predicting Class of Income on Census Data – Part 3 00:05:04
4. Chapter 4 : Implement Clustering Techniques in TensorFlow
   • Introduction to K-Means Clustering 00:06:45
   • Apply K-Means Clustering on the Blob Dataset Part - 1 00:04:46
   • Apply K-Means Clustering on the Blob Dataset Part - 2 00:05:44
5. Chapter 5 : Create Your Own Artificial Neural Network
   • What is Deep Learning? 00:09:26
   • Part 1 - Data Preprocessing 00:10:15
   • Part 2 - Let's Create the ANN 00:04:23
   • Part 3 - Making Predictions and Evaluating Models 00:03:36
6. Chapter 6 : Build Convolutional Neural Network Using Image Dataset
   • What Is a Convolutional Neural Network? 00:10:44
   • Part 1 - Import MNIST Data from TensorFlow 00:06:29
   • Part 2 - Create Placeholders and Layers 00:04:36
   • Part 3 - Optimize and Run Sessions 00:06:35
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