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TORRENT DETAILS

[FreeCoursesOnline.Me] [Packt] Artificial Intelligence In 3 Hours [FCO]

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	efba4af6518720cb573c586e53f7651575520ab0
Size:	858.2 MB in 50 files
Release Date(s):	Torrent Release: 12-04-2019 by user
Swarm:	0 Seeds & 0 Peers
Vote:	No votes yet.

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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FILE LIST

Filename		Size
	01.Understanding Supervised Machine Learning/0101.The Course Overview.mp4	44.6 MB
	01.Understanding Supervised Machine Learning/0102.Breast Cancer Classification.mp4	12.9 MB
	01.Understanding Supervised Machine Learning/0103.Breast Cancer Dataset Exploration.mp4	39.8 MB
	01.Understanding Supervised Machine Learning/0104.Breast Cancer Dataset Preprocessing.mp4	15.3 MB
	01.Understanding Supervised Machine Learning/0105.Intuition Behind K-Nearest Neighbors Algorithm.mp4	7.2 MB
	01.Understanding Supervised Machine Learning/0106.Creating the KNN Algorithm.mp4	3.1 MB
	01.Understanding Supervised Machine Learning/0107.Grid Search and Hyper Parameters.mp4	10 MB
	01.Understanding Supervised Machine Learning/0108.Training the Model.mp4	4.1 MB
	01.Understanding Supervised Machine Learning/0109.Model evaluation.mp4	17.4 MB
	01.Understanding Supervised Machine Learning/0110.What Is Supervised Learning.mp4	15.8 MB
	02.Exploring Unsupervised Machine Learning/0201.Customer Segmentation.mp4	6.7 MB
	02.Exploring Unsupervised Machine Learning/0202.Mall Customers Dataset Exploration.mp4	7.6 MB
	02.Exploring Unsupervised Machine Learning/0203.Mall Customers Dataset Preprocessing.mp4	132 MB
	02.Exploring Unsupervised Machine Learning/0204.Why Do We Split Dataset into Training and Testing Parts.mp4	5.9 MB
	02.Exploring Unsupervised Machine Learning/0205.Intuition Behind K-Means Algorithm.mp4	33.8 MB
	02.Exploring Unsupervised Machine Learning/0206.Creating K-Means Algorithm.mp4	34.1 MB
	02.Exploring Unsupervised Machine Learning/0207.Training and Evaluating the KMeans Model.mp4	11.8 MB
	02.Exploring Unsupervised Machine Learning/0208.Cluster Analysis.mp4	123.4 MB
	03.Working with Deep Learning/0301.Boston Housing - Task Explanation.mp4	3.5 MB
	03.Working with Deep Learning/0302.Boston Housing Dataset Exploration.mp4	30.8 MB
	03.Working with Deep Learning/0303.Boston Housing Dataset Preprocessing.mp4	15.4 MB
	03.Working with Deep Learning/0304.Explaining the Fully Connected Network.mp4	15.1 MB
	03.Working with Deep Learning/0305.Building the Model.mp4	6.3 MB
	03.Working with Deep Learning/0306.Compiling the Model ( Choosing the Loss Function and Metrics).mp4	5.3 MB
	03.Working with Deep Learning/0307.Training the Model.mp4	5.3 MB
	03.Working with Deep Learning/0308.Model Evaluation.mp4	7.5 MB
	04.Image Classification with Convolutional Neural Networks/0401.Image Classification.mp4	6.3 MB
	04.Image Classification with Convolutional Neural Networks/0402.MNIST Dataset Exploration.mp4	18.4 MB
	04.Image Classification with Convolutional Neural Networks/0403.MNIST Dataset Preprocessing.mp4	16.4 MB
	04.Image Classification with Convolutional Neural Networks/0404.How Do Convolutional Neural Networks Work.mp4	13 MB
	04.Image Classification with Convolutional Neural Networks/0405.Building the Model.mp4	9.5 MB
	04.Image Classification with Convolutional Neural Networks/0406.Compiling the Model( Choosing the Loss Function and Metrics).mp4	2.7 MB
	04.Image Classification with Convolutional Neural Networks/0407.Training the Model.mp4	3.5 MB
	04.Image Classification with Convolutional Neural Networks/0408.Model Evaluation.mp4	7 MB
	04.Image Classification with Convolutional Neural Networks/0409.Explaining What Tasks Can Be Solved Using CNNs.mp4	7.6 MB
	05.Working With Deep Reinforcement Learning/0501.Explaining What Reinforcement Learning Is.mp4	11 MB
	05.Working With Deep Reinforcement Learning/0502.States, Rewards, and Value.mp4	13 MB
	05.Working With Deep Reinforcement Learning/0503.Explaining the Deep Q Network.mp4	10 MB
	05.Working With Deep Reinforcement Learning/0504.Experience Replay.mp4	6.7 MB
	05.Working With Deep Reinforcement Learning/0505.What Is a Policy.mp4	11.6 MB
	05.Working With Deep Reinforcement Learning/0506.Building the Model.mp4	5.7 MB
	05.Working With Deep Reinforcement Learning/0507.Compiling the Model.mp4	1.5 MB
	05.Working With Deep Reinforcement Learning/0508.Training the Model.mp4	6.2 MB
	05.Working With Deep Reinforcement Learning/0509.Game Playing with the Trained Model.mp4	57.8 MB
	05.Working With Deep Reinforcement Learning/0510.Real World Problems Solved with Reinforcement Learning.mp4	34.8 MB
	Discuss.FTUForum.com.html	31.9 KB
	Exercise Files/exercise_files.zip	976.6 KB
	FreeCoursesOnline.Me.html	108.3 KB
	FTUForum.com.html	100.4 KB
	How you can help Team-FTU.txt	235 B

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