From this course you can learn Object-Oriented Programming from basics to advanced concepts. All code examples in the course are written in Java but that's doesn't mean you can't apply the knowledge from this course in other programming languages. You can easily use the knowledge from this course in any language if you want to build applications with the help of object-oriented programming approach. There are a lot of other courses in this topic. So, why would you choose exactly this course?

Random Subspace Ensemble is a machine learning algorithm that combines the predictions from multiple decision trees trained on different subsets of columns in the training dataset. Randomly varying the columns used to train each contributing member of the ensemble has the effect of introducing diversity into the ensemble and, in turn, can lift performance over using a single decision tree. It is related to other ensembles of decision trees such as bootstrap aggregation (bagging) that creates trees using different samples of rows from the training dataset, and random forest that combines ideas from bagging and the random subspace ensemble. Although decision trees are often used, the general random subspace method can be used with any machine learning model whose performance varies meaningfully with the choice of input features. In this tutorial, you will discover how to develop random subspace ensembles for classification and regression.

Online Courses Udemy | Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing BESTSELLER 4.5 (2,250 ratings) Created by Jose Portilla  English [Auto-generated], Italian [Auto-generated] Preview this course  - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes

Online Courses Udemy - Computer Vision: Python OCR & Object Detection Quick Starter, Quick Starter for Optical Character Recognition, Image Recognition Object Detection and Object Recognition using Python Hot & New Created by Abhilash Nelson English Students also bought Python 3.8 for beginners 2020 Docker for Beginners Python Programming from Basics to Advanced FL Studio 20 - EDM Masterclass Music Production in FL Studio Microsoft Azure Data Lake Storage Service (Gen1 & Gen2) Geospatial Data Analyses & Remote Sensing: 4 Classes in 1 Preview this course GET COUPON CODE Description Hi There! welcome to my new course'Optical Character Recognition and Object Recognition Quick Start with Python'. This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document.

This is an edited version of my undergraduate thesis, submitted to the Harvard Mathematics Department in May 2020. It differs from the original thesis in one major respect, namely that this version omits the proofs of a number of theorems that are readily-available in other expositions. Whereas the original version reproduced these proofs in full, this version simply contains references to these proofs in other works. This thesis is built upon an extensive body of prior work in learning theory, graph theory, differential geometry, and manifold learning. In particular, I would like to thank Professors Lorenzo Rosasco and Tomaso Poggio for their lectures on statistical learning theory, Professor Daniel Spielman for his notes on spectral graph theory, Professor Yaiza Canzani for her notes on analysis on manifolds, and Professor Mikhail Belkin for his work on manifold learning. Finally, I wish to thank those people without whom I could never have written this thesis: my family, friends, and wonderful advisor Professor Arjun Manrai. Unlike the manifolds discussed herein, their support was truly boundless. I hope you enjoy and learn something from this thesis! If you have comments, corrections, or would like to contact me for anything else, feel free to email me.

The purpose of this tutorial is to help individuals use the \underline{FireCommander} game environment for research applications. The FireCommander is an interactive, probabilistic joint perception-action reconnaissance environment in which a composite team of agents (e.g., robots) cooperate to fight dynamic, propagating firespots (e.g., targets). In FireCommander game, a team of agents must be tasked to optimally deal with a wildfire situation in an environment with propagating fire areas and some facilities such as houses, hospitals, power stations, etc. The team of agents can accomplish their mission by first sensing (e.g., estimating fire states), communicating the sensed fire-information among each other and then taking action to put the firespots out based on the sensed information (e.g., dropping water on estimated fire locations). The FireCommander environment can be useful for research topics spanning a wide range of applications from Reinforcement Learning (RL) and Learning from Demonstration (LfD), to Coordination, Psychology, Human-Robot Interaction (HRI) and Teaming. There are four important facets of the FireCommander environment that overall, create a non-trivial game: (1) Complex Objectives: Multi-objective Stochastic Environment, (2)Probabilistic Environment: Agents' actions result in probabilistic performance, (3) Hidden Targets: Partially Observable Environment and, (4) Uni-task Robots: Perception-only and Action-only agents. The FireCommander environment is first-of-its-kind in terms of including Perception-only and Action-only agents for coordination. It is a general multi-purpose game that can be useful in a variety of combinatorial optimization problems and stochastic games, such as applications of Reinforcement Learning (RL), Learning from Demonstration (LfD) and Inverse RL (iRL).

Udemy Online Courses - Create a SHMUP with Unity 3D, Build a Shoot em up game for mobile with C# and Unity 3D 4.7 (50 ratings), Created by Romi Fauzi, English [Auto-generated] Create a complete SHMUP game like Skyforce Upgrade (include timed upgrades) and Save features. Upgrade (include timed upgrades) and Save features. In this course we will create a full Shoot Em Up game (Skyforce, Raiden) from scratch in Unity. You will learn about object oriented programming and have an overall better understanding of C#. We will provide you with all the assets needed to create the game (including 3d models, audio), feel free to use these assets in your own games.

This is currently in an Early Bird Beta access, meaning we are still going to be continually adding content to the course (even though we are already at over 20 hours of content!) Since we're still adding content and taking student feedback as we complete the course through the start of 2021, students who enroll now will get access to a wide variety of benefits! You will get exclusive access to weekly live video streams where we will go through interactive machine learning projects! You'll be able to directly ask questions during the streams that will coincide with section launches corresponding to new machine learning algorithms added to the course content! These weekly streams will also include live Q&A with the instructor of the course, Jose Portilla. We will also be taking in student feedback to shape certain upcoming streaming projects.

In this age of big data, companies worldwide need to sift through the avalanche of information at their disposal to enhance their products, services and overall profitability. Many companies rely on programming languages like Python and the advancements made in artificial intelligence (AI) and data science to get that job done. Right now, you can save hundreds on The Ultimate Python & Artificial Intelligence Certification Bundle, featuring nine in-depth courses and 38 hours of video content that catches you up to speed on everything Python, AI and data science.

The applications of artificial intelligence (AI) have become ubiquitous over the last decade, transforming the way we work, play, and interact with the world. AI systems recommend what you should watch on Netflix, recognize your biometric data for authentication, and assist you during "live" chats with customer service. These examples highlight common applications of AI systems, but AI can also promote security in our data and systems. The U.S. Chamber of Commerce's Cyber, Intelligence, and Supply Chain Security Division hosted a discussion today on the applications of AI in cybersecurity during its Now Next webinar series, and dove into how businesses can leverage AI in their security efforts. "Humans are great at intuition and creativity. The challenge in cyber is that you sometimes have very subtle signals pointing to anomalies, and you must move beyond human scale to see that drift from normality in complex enterprises. Moreover, you must understand those subtle signals and respond to them at machine speed. This is where the power of AI potentially provides the most value," said Albert Biketi, Vice President, Security Business at Splunk.