Packt | Artificial Intelligence in 3 Hours [FCO]
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Files
| 0101.The Course Overview.mp4 | 44.6 MB | ||
| 0102.Breast Cancer Classification.mp4 | 12.9 MB | ||
| 0103.Breast Cancer Dataset Exploration.mp4 | 39.8 MB | ||
| 0104.Breast Cancer Dataset Preprocessing.mp4 | 15.3 MB | ||
| 0105.Intuition Behind K-Nearest Neighbors Algorithm.mp4 | 7.2 MB | ||
| 0106.Creating the KNN Algorithm.mp4 | 3.1 MB | ||
| 0107.Grid Search and Hyper Parameters.mp4 | 10 MB | ||
| 0108.Training the Model.mp4 | 4.1 MB | ||
| 0109.Model evaluation.mp4 | 17.4 MB | ||
| 0110.What Is Supervised Learning.mp4 | 15.8 MB | ||
| 0201.Customer Segmentation.mp4 | 6.7 MB | ||
| 0202.Mall Customers Dataset Exploration.mp4 | 7.6 MB | ||
| 0203.Mall Customers Dataset Preprocessing.mp4 | 132 MB | ||
| 0204.Why Do We Split Dataset into Training and Testing Parts.mp4 | 5.9 MB | ||
| 0205.Intuition Behind K-Means Algorithm.mp4 | 33.8 MB | ||
| 0206.Creating K-Means Algorithm.mp4 | 34.1 MB | ||
| 0207.Training and Evaluating the KMeans Model.mp4 | 11.8 MB | ||
| 0208.Cluster Analysis.mp4 | 123.4 MB | ||
| 0301.Boston Housing - Task Explanation.mp4 | 3.5 MB | ||
| 0302.Boston Housing Dataset Exploration.mp4 | 30.8 MB | ||
| 0303.Boston Housing Dataset Preprocessing.mp4 | 15.4 MB | ||
| 0304.Explaining the Fully Connected Network.mp4 | 15.1 MB | ||
| 0305.Building the Model.mp4 | 6.3 MB | ||
| 0306.Compiling the Model ( Choosing the Loss Function and Metrics).mp4 | 5.3 MB | ||
| 0307.Training the Model.mp4 | 5.3 MB | ||
| 0308.Model Evaluation.mp4 | 7.5 MB | ||
| 0401.Image Classification.mp4 | 6.3 MB | ||
| 0402.MNIST Dataset Exploration.mp4 | 18.4 MB | ||
| 0403.MNIST Dataset Preprocessing.mp4 | 16.4 MB | ||
| 0404.How Do Convolutional Neural Networks Work.mp4 | 13 MB | ||
| 0405.Building the Model.mp4 | 9.5 MB | ||
| 0406.Compiling the Model( Choosing the Loss Function and Metrics).mp4 | 2.7 MB | ||
| 0407.Training the Model.mp4 | 3.5 MB | ||
| 0408.Model Evaluation.mp4 | 7 MB | ||
| 0409.Explaining What Tasks Can Be Solved Using CNNs.mp4 | 7.6 MB | ||
| 0501.Explaining What Reinforcement Learning Is.mp4 | 11 MB | ||
| 0502.States, Rewards, and Value.mp4 | 13 MB | ||
| 0503.Explaining the Deep Q Network.mp4 | 10 MB | ||
| 0504.Experience Replay.mp4 | 6.7 MB | ||
| 0505.What Is a Policy.mp4 | 11.6 MB | ||
| 0506.Building the Model.mp4 | 5.7 MB | ||
| 0507.Compiling the Model.mp4 | 1.5 MB | ||
| 0508.Training the Model.mp4 | 6.2 MB | ||
| 0509.Game Playing with the Trained Model.mp4 | 57.8 MB | ||
| 0510.Real World Problems Solved with Reinforcement Learning.mp4 | 34.8 MB | ||
| Discuss.FTUForum.com.html | 31.9 KB | ||
| FTUForum.com.html | 100.4 KB | ||
| FreeCoursesOnline.Me.html | 108.3 KB | ||
| How you can help Team-FTU.txt | 204.8 B | ||
| Torrent Downloaded From GloDls.to.txt | 102.4 B | ||
| [TGx]Downloaded from torrentgalaxy.org.txt | 512 B | ||
| exercise_files.zip | 976.6 KB | ||
| ▲ 52 total files | |||
Description
By : Luka Anicin
Released : Friday, March 29, 2019 [New Release!]
Torrent Contains : 52 Files, 6 Folders
Course Source : https://www.packtpub.com/application-development/artificial-intelligence-3-hours-video
Get up and running with AI in less than 3 hours
Video Details
ISBN 9781838646196
Course Length 3 hour 5 minutes
Table of Contents
• UNDERSTANDING SUPERVISED MACHINE LEARNING
• EXPLORING UNSUPERVISED MACHINE LEARNING
• WORKING WITH DEEP LEARNING
• IMAGE CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS
• WORKING WITH DEEP REINFORCEMENT LEARNING
Video Description
What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common? They are all complex real-world problems being solved with applications of intelligence (AI). This course provides you with a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.
In this course, you will progress to advanced AI techniques and concepts, and work on real-life datasets on how training works, model evaluation and how to process their first dataset in Supervised and Unsupervised Learning. You will be introduced to neural networks, machine Learning, and Deep Learning. You will understand the concept of Artificial Intelligence and how it is applicable in the enterprise context. You will then cover the basics of machine learning relate how Deep Learning and AI fits with them.
Finally, you will see understand, Deep Reinforcement Learning and will be able to implement AI in your applications.
Style and Approach
A comprehensive course that takes you through AI in 3 hours. It is packed with step by step instructions and working examples.
What You Will Learn
• Build your own Machine Learning classifier to predict the type of breast cancer, after creating this system, you will have an understanding of classification problems and you will help patients and doctors at the same time!
• Develop a tool that helps sellers and marketing agencies to target their customers. After completing this project you will be ready to apply your machine learning knowledge to most unsupervised problems!
• Discover an exciting world of Neural Networks hands-on by building a model that predicts prices of houses in the Boston area. This will give you a great introduction to regression problems and Deep Learning in general.
• Creating your own model that is able to recognize objects on a raw image with superhuman accuracy. Here you will learn how to build state of the art models for image recognition tasks.
• Build an Artificial Intelligence agent that solves atari games in front of you! Learn how Alpha-Go has beaten the best Go player in the world.
Authors
Luka Anicin
Luka Anicin is a passionate and experienced Artificial Intelligence Engineer and he loves to share knowledge with others! In his career he had worked on many different and exciting projects where he had an opportunity to apply some of currently most advanced AI algorithms. Some project that Luka has in his portfolio: Electricity grid optimization for multiple countries, OCR for car chassis numbers, deploying chatbots for multiple customers, object detection pipeline for image search system, etc.
He is putting his knowledge and experience into one, to create courses for people interested in Data Science.
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