Showing posts with label Machine Learning. Show all posts
Showing posts with label Machine Learning. Show all posts

(Download) - Hands-On Machine Learning (incl. Deep Learning) with R


Understand machine learning models and how to implement them in R from an expert in Data Science. (All code included)

What you'll learn

  • You will learn to build state-of-the-art Machine Learning models with R.
  • We will implement Deep Learning models with Keras for Regression and Classification tasks.
  • Regression Models (e.g. univariate, polynomial, multivariate)
  • Regularization Techniques
  • Classification Models (e.g. Confusion Matrix, ROC, Logistic Regression, Decision Trees, Random Forests, SVM, Ensemble Learning)
  • Association Rules (e.g. Apriori)
  • Clustering techniques (e.g. kmeans, hierarchical clustering, dbscan)
  • Dimensionality Reduction techniques (e.g. Principal Component Analysis, Factor Analysis)
  • Reinforcement Learning techniques (e.g. Upper Confidence Bound)
  • You will know how to evaluate your model, what underfitting and overfitting is, why resampling techniques are important, and how you can split your dataset into parts (train/validation/test).
  • We will understand the theory behind deep neural networks.
  • We will understand and implement convolutional neural networks - the most powerful technique for image recognition.

Requirements

  • Basic R Programming knowledge

Description

Did you ever wonder how machines "learn" - in this course you will find out.

We will cover all fields of Machine Learning: regression and classification techniques, clustering, association rules, reinforcement learning, and, finally, Deep Learning.

For each field, different algorithms are shown in detail: their core concepts are presented in 101 sessions. Here, you will understand how the algorithm works. Then we implement it together in lab sessions. We develop code, before I encourage you to work on exercise on your own, before you watch my solution examples. With this knowledge you can clearly identify a problem at hand and develop a plan of attack to solve it.

You will understand the advantages and disadvantages of different models and when to use which one. Furthermore, you will know how to take your knowledge into the real world.

You will get access to an interactive learning platform that will help you to understand the concepts much better.

In this course code will never come out of thin air via copy/paste. We will develop every important line of code together and I will tell you why and how we implement it.

Take a look at some sample lectures. Or visit some of my interactive learning boards. Furthermore, there is a 30 day money back warranty, so there is no risk for you taking the course right now. Don’t wait. See you in the course.

Who this course is for:

  • R beginners and professionals with interest in Machine Learning

[Download] - A Beginner's Guide To Machine Learning with Unity



Advanced games AI with genetic algorithms, neural networks & Q-learning in C# and Tensorflow for Unity

What you'll learn

  • Build a genetic algorithm from scratch in C#.
  • Build a neural network from scratch in C#.
  • Setup and explore the Unity ML-Agents plugin.
  • Setup and use Tensorflow to train game characters.
  • Apply their newfound knowledge of machine learning to integrate contemporary research ideas in the field into their own projects.
  • Distill the mathematics and statistic behind machine learning to working program code.
  • Use a Proximal Policy Optimisation to train a neural network

Requirements

  • You should be familiar with the Unity Game Engine.
  • You should have a working knowledge of C#.
  • You should have a healthy appreciation for mathematics and statistics.

Description

What if you could build a character that could learn while it played?  Think about the types of game play you could develop where the enemies started to outsmart the player. This is what machine learning in games is all about. In this course we will discover the fascinating world of artificial intelligence beyond the simple stuff and examine the increasingly popular domain of machines that learn to think for themselves.

In this course, Penny introduces the popular machine learning techniques of genetic algorithms and neural networks using her internationally acclaimed teaching style and knowledge from a Ph.D in game character AI and over 25 years experience working with games and computer graphics.  In addition she's written two award winning books on games AI and two others best sellers on Unity game development. Through-out the course you will follow along with hands-on workshops designed to teach you about the fundamental machine learning techniques distilling the mathematics in a way that the topic becomes accessible to the most noob of novices.  

Learn how to program and work with:

  • genetic algorithms;
  • neural networks;
  • human player captured training sets;
  • reinforcement learning;
  • Unity's ML-Agent plugin; and,
  • Tensorflow;

Contents and Overview

The course starts with a thorough examination of genetic algorithms that will ease you into one of the simplest machine learning techniques that is capable of extraordinary learning. You'll develop an agent that learns to camouflage, a flappy bird inspired application in which the birds learn to make it through a maze and environment sensing bots that learn to stay on a platform.

Following this you'll dive right into creating your very own neural network in C# from scratch.  With this basic neural network you will find out how to train behaviour, capture and use human player data to train an agent and teach a bot to drive.  In the same section you'll have the Q-learning algorithm explained before integrating it into your own applications.

By this stage you'll feel confident with the terminology and techniques used throughout the deep learning community and ready to tackle Unity's experimental ML-Agents. Together with Tensorflow you'll be throwing agents in the deep end and reinforcing their knowledge to stay alive in a variety of game environment scenarios.

By the end of the course you'll have a well equiped toolset of basic and solid machine learning algorithms and applications that will see you able to decipher the latest research publications and integrate the latest developments into your work while keeping abreast of Unity's ML-Agents as they evolve from experimental to production release.

What students are saying about this course:

  • Absolutely the best beginner to Advanced course for Neural Networks/ Machine Learning if you are a game developer that uses C# and Unity. BAR NONE x Infinity.
  • A perfect course with great math examples and demonstration of the TensorFlow power inside Unity. After this course, you will get the strong basic background in the Machine Learning.
  • The instructor is very engaging and knowledgeable. I started learning from the first lesson and it never stopped. If you are interested in Machine Learning , take this course.

Who this course is for:

  • Anyone wanting to learn about the potential of machine learning in games.
  • Anyone wanting a deeper understanding of the algorithms and theories underlying Unity's ML-Agents.
  • Anyone wanting to know how to setup and work with ML-Agents.
Created by Penny de Byl
Last updated 12/2018
English
English [Auto-generated]

12 hours on-demand video


https://www.udemy.com/machine-learning-with-unity/