by Eduonix Learning Solutions | Released February 2018 | ISBN: 9781789138245

A complete guide to master machine learning concepts and create real-world ML solutionsAbout This VideoStart at the very beginning and delve right into machine learning, before breaking down the most important concepts principles.The course does require you to have a mathematical background as machine learning relies heavily on mathematical concepts.In DetailIf you've ever wanted the Jetsons to be real, well we aren't that far off from a future like that. If you've ever chatted with automated robots, then you've definitely interacted with machine learning. From self-driving cars to AI bots, machine learning is slowly spreading its reach and making our devices smarter. Artificial intelligence is the future of computers, where your devices will be able to decide what is right for you. Machine learning is the core for having a futuristic reality where robot maids and robodogs exist. Machine learning includes the algorithms that allow the computers to think and respond, as well as manipulate the data depending on the scenario that's placed before them. So, if you've ever wanted to play a role in the future of technology development, then here's your chance to get started with machine learning. Because machine learning is complex and tough, we've designed a course to help break it down into more simple concepts that are easier to understand. It also requires you to have some experience with Python principles which will be required when we put the algorithms to test in actual real-world Python projects. The course covers a number of different machine learning algorithms such as supervised learning, unsupervised learning, reinforced learning and even neural networks. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that's not all. At the end of each unit, the course includes quizzes to help you evaluate your learning on the subject. Show and hide more Publisher Resources Download Example Code

1. Chapter 1 : An Introduction to Machine Learning
   • Introduction 00:00:58
   • What is Machine Learning? 00:10:53
   • Types and Applications of ML 00:25:46
   • AI vs ML 00:09:43
   • Essential Math for ML and AI 00:17:04
2. Chapter 2 : Supervised Learning - part 1
   • Introduction to Supervised Learning 00:13:38
   • Linear Methods for Classification 00:16:35
   • Linear Methods for Regression 00:11:52
   • Support Vector Machines 00:15:42
   • Basis Expansions 00:11:00
   • Model Selection Procedures 00:13:58
   • Bonus! Supervised Learning Project in Python Part 1 00:15:24
   • Bonus! Supervised Learning Project in Python Part 2 00:15:24
3. Chapter 3 : Unsupervised Learning
   • Introduction to Unsupervised Learning 00:11:37
   • Association Rules 00:14:14
   • Cluster Analysis 00:13:19
   • Reinforcement Learning 00:16:34
   • Bonus! KMeans Clustering Project 00:14:15
4. Chapter 4 : Neural Networks
   • Introduction to Neural Networks 00:12:26
   • The Perceptron 00:10:21
   • The Backpropagation Algorithm 00:12:19
   • Training Procedures 00:13:37
   • Convolutional Neural Networks 00:15:55
5. Chapter 5 : Real World Machine Learning
   • Introduction to Real World ML 00:10:35
   • Choosing an Algorithm 00:08:44
   • Design and Analysis of ML Experiments) 00:10:23
   • Common Software for ML 00:10:46
6. Chapter 6 : Final Project
   • Setting up OpenAI Gym 00:12:44
   • Building and Training the Network Part 1 00:16:15
   • Building and Training the Network Part 2 00:21:54
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