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Machine Learning: Instructional Materials


How to Make AIs Target Objects with Unity ML Agents

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We often hear in the news about this thing called "machine learning" and how computers are "learning" to perform certain tasks. From the examples we see, it almost seems like magic when a computer creates perfect landscapes from thin air or makes a painting talk. But what is often overlooked, and what we want to cover in this tutorial, is that machine learning can be used in video game creation as well. In other words, we can use machine learning to make better and more interesting video games by training our AIs to perform certain tasks automatically with machine learning algorithms. This tutorial will show you how we can use Unity ML agents to make an AI target and find a game object. More specifically, we'll be looking at how to customize the training process to create an AI with a very specific proficiency in this task. Through this, you will get to see just how much potential machine learning has when it comes to making AI for video games. So, without further ado, let's get started and learn how to code powerful AIs with the power of Unity and machine learning combined!


A Gentle Introduction to Probabilistic Programming Languages

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I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Probabilistic thinking is an incredibly valuable tool for decision making. From economists to poker players, people that can think in terms of probabilities tend to make better decisions when faced with uncertain situations.


Machine Learning for Absolute Beginners - Level 3

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Machine Learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and ML experts to develop products, features, and projects that will help them unleash the power of machine learning. As a result, a data scientist is one of the top ten wanted jobs worldwide! The "Machine Learning for Absolute Beginners" training program is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. The training is divided into multiple levels, and each level is covering a group of related topics for a continuous step by step learning path.


Step By Step Guide To Stabilize Facial Landmarks In A Video Using Dlib

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The human face has been a topic of interest for deep learning engineers for quite some time now. Understanding the human face not only helps in facial recognition but finds applications in facial morphing, head pose detection and virtual makeovers. If you are a regular user of social media apps like Instagram or Snapchat, have you wondered how the filters fit perfectly for each face? Though every face on the planet is unique, these filters seem to magically align on your nose, lips and eyes. These filters or face-swapping applications make use of facial landmarks.


Logistic Regression using SAS - Indepth Predictive Modeling

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What is this course all about? This course is all about credit scoring / logistic regression model building using SAS. There course promises to explain concepts in a crystal clear manner. It goes through the practical issue faced by analyst. How to clarify objective and ensure data sufficiency?


Python for Data Science and Machine Learning beginners

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Free Coupon Discount - Python for Data Science and Machine Learning beginners, produced by -- When maths meet coding Created by Jay Shankar Bhatt Preview this Udemy Course GET COUPON CODE 100% Off Udemy Coupon .


Deep Reinforcement Learning 2.0

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Online Courses Udemy - Deep Reinforcement Learning 2.0, The smartest combination of Deep Q-Learning, Policy Gradient, Actor Critic, and DDPG Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team English [Auto] Students also bought Unsupervised Deep Learning in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Data Science: Natural Language Processing (NLP) in Python Recommender Systems and Deep Learning in Python Cutting-Edge AI: Deep Reinforcement Learning in Python Ensemble Machine Learning in Python: Random Forest, AdaBoost Preview this course GET COUPON CODE Description Welcome to Deep Reinforcement Learning 2.0! In this course, we will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field). To approach this model the right way, we structured the course in three parts: Part 1: Fundamentals In this part we will study all the fundamentals of Artificial Intelligence which will allow you to understand and master the AI of this course. These include Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic and more.


A Practical Introduction to Early Stopping in Machine Learning

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In this article, we will focus on adding and customizing Early Stopping in our machine learning model and look at an example of how we do this in practice with Keras and TensorFlow 2.0. In machine learning, early stopping is one of the most widely used regularization techniques to combat the overfitting issue. Early Stopping monitors the performance of the model for every epoch on a held-out validation set during the training, and terminate the training conditional on the validation performance. Early Stopping is a very different way to regularize the machine learning model. The way it does is to stop training as soon as the validation error reaches a minimum.


Theory & Hands-On Artificial Neural Networks Udemy Course

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At the end of the Course you will understand the basics of Artificial Neural Networks. The course will have step by step guidance for Artificial Neural network development in Python. I have 9 years of work experience as a Researcher, Senior Lecturer, Project Supervisor & Engineer. I have completed a MSc in Artificial Intelligence.


Google Earth Engine for Machine Learning & Change Detection

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Google Earth Engine for Machine Learning & Change Detection Students will gain access to and a thorough knowledge of the Google Earth Engine platform. Get introduced and advance JavaScript skills on Google Earth Engine platform. This course is designed to take users who use GIS for basic geospatial data/GIS/Remote Sensing analysis to perform more advanced geospatial analysis tasks using a variety of different data and applying Machine Learning state of the art algorithms. In addition to improving your skills in JavaScript, this course will make you proficient in Google Earth Engine for land use and land cover (LULC) mapping and change detection. As a result, you will be introduced to the exciting capabilities of Google Earth Engine which is a global leader for cloud computing in Geosciences!