Looking for Artificial Intelligence Tutorial to learn introduction to artificial intelligence? Grab the list of Best Artificial Intelligence Courses Online, Tutorials, and Training are offered by a number of massive open online course (MOOC) providers like Udemy, Coursera, and edX. Artificial Intelligence (AI) and machine intelligence are the most booming topics in every industry now. Some of these popular MOOC providers offer some in-depth artificial intelligence programs. The list of the Best Artificial Intelligence Certification is often taught by industry top AI researchers or experts and you will learn the best applications of artificial intelligence.

Are you looking for the best online masters programs? Here is the list of the Best Edureka Online Masters Programs will make you proficient in tools, systems, and skills required to build specific professional expertise like Data Scientist, DevOps Engineers, Big Data Architect, Could Architect, Full Stack Web Development, Business Intelligence, Data Analyst or as a Machine Learning expert. According to Edureka, they stand by you all the way to ensure that you achieve your learning goals. Edureka provides instructor-led Live Online Classes as per your convenience. You will have a Personal Learning Manager with Lifetime Access in your enrolled courses. Description: Data Science Master's Program makes you proficient in the tools and systems used by Data Science Professionals.

Lasse Rouhiainen is a best-selling author and international expert on artificial intelligence, disruptive technologies and digital marketing. Finnish in origin but based in Spain, Lasse focuses his work on investigating how companies and society in general can better adapt to, and benefit from, artificial intelligence. Lasse has given keynote presentations, seminars and workshops in more than 16 countries around the world and holds frequent conferences at several universities internationally. He has also provided training to thousands of students and businesses through online e-learning courses. Lasse has been a speaker at renowned seminars such as Mobile World Capital and TEDx, and has worked with top brands and institutions such as Michelin, Össur and the European Union Intellectual Property Office.

Link: Bayesian Machine Learning in Python: A/B Testing coupon code udemy Traditional A/B testing has been around for a long time, and it's full of approximations and confusing definitions. In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things. First, we'll see if we can improve on ... Bestseller by Lazy Programmer Inc. What you'll learn Use adaptive algorithms to improve A/B testing performance Understand the difference between Bayesian and frequentist statistics Apply Bayesian methods to A/B testing Description This course is all about A/B testing. A/B testing is used everywhere.

Link: Machine Learning A-Z: Hands-On Python & R In Data Science coupon code udemy Machine Learning A-Z: Hands-On Python & R In Data Science 4.5 (107,137 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. Bestseller by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, SuperDataScience Support What you'll learn Master Machine Learning on Python & R Have a great intuition of many Machine Learning models Make accurate predictions Make powerful analysis Make robust Machine Learning models Create strong added value to your business Use Machine Learning for personal purpose Handle specific topics like Reinforcement Learning, NLP and Deep Learning Handle advanced techniques like Dimensionality Reduction Know which Machine Learning model to choose for each type of problem Build an army of powerful Machine Learning models and know how to combine them to solve any problem Description Interested in the field of Machine Learning? Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning.

Link: 2020 AWS SageMaker, AI and Machine Learning - With Python coupon code udemy The author of this exam, Frank Kane, is a popular machine learning instructor on Udemy who passed the AWS Certified Machine Learning exam himself on the first try - as well as the AWS Certified Big Data Specialty exam, which the Machine Learning exam builds upon. Bestseller by Chandra Lingam What you'll learn Learn AWS Machine Learning algorithms, Predictive Quality assessment, Model Optimization Integrate predictive models with your application using simple and secure APIs Convert your ideas into highly scalable products in days Practice test and resources to gain AWS Certified Machine Learning - Specialty Certification Description Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep *** UPDATE JAN-2020 Timed Practice Test and additional lectures for Exam Preparation added For Practice Test, look for the section: 2020 Practice Exam - AWS Certified Machine Learning Specialty For exam overview, gap analysis and preparation strategy, look for 2020 - Overview - AWS Machine Learning Specialty Exam *** *** UPDATE DEC-2019 Third update for this month!!! AWS Certified Machine Learning Specialty Exam Overview and Preparation Strategies lectures added to the course! Timed Practice Exam is coming soon! Also added, two new lectures that gives an overview of all SageMaker Built-in Algorithms, Frameworks and Bring-Your-Own Algorithm Supports Look for lectures starting with 2020 *** *** UPDATE DEC-2019. In the Neural Network and Deep Learning section, we will look at the core concepts behind neural networks, why deep learning is popular these days, different network architectures and hands-on labs to build models using Keras, TensorFlow, Apache MxNet: 2020 Deep Learning and Neural Networks *** *** UPDATE DEC-2019.

Many imbalanced classification tasks require a skillful model that predicts a crisp class label, where both classes are equally important. An example of an imbalanced classification problem where a class label is required and both classes are equally important is the detection of oil spills or slicks in satellite images. The detection of a spill requires mobilizing an expensive response, and missing an event is equally expensive, causing damage to the environment. One way to evaluate imbalanced classification models that predict crisp labels is to calculate the separate accuracy on the positive class and the negative class, referred to as sensitivity and specificity. These two measures can then be averaged using the geometric mean, referred to as the G-mean, that is insensitive to the skewed class distribution and correctly reports on the skill of the model on both classes. In this tutorial, you will discover how to develop a model to predict the presence of an oil spill in satellite images and evaluate it using the G-mean metric. Develop an Imbalanced Classification Model to Detect Oil Spills Photo by Lenny K Photography, some rights reserved. In this project, we will use a standard imbalanced machine learning dataset referred to as the "oil spill" dataset, "oil slicks" dataset or simply "oil."

Machine Learning with Python is the new arenaof modern Artificial Intelligence; Machine Learningis new trend in Information Technology. We here at Codec Networks will ensure that you are not leftbehind in this fast moving world of Big Data and its uses in sense of Analytics and developing models in Machine Learning, and help get started in this field. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course isfun and exciting, but at the same time we dive deep into Machine Learning.

MATLAB (matrix laboratory) is one of the fundamental and leading programming language and is a must learn skill for anyone who want to develop a career in engineering, science or related fields. Excellent MATLAB programming skills is therefore a crucial factor in making or breaking your career. This course is designed from a perspective of a student who has no prior knowledge of MATLAB. The course starts from the very basic concepts and then built on top of those basic concepts and move towards more advanced topics such as visualization, exporting and importing of data, advance data types and data structures and advance programming constructs. To get the real feel of MATLAB in solving and analyzing real life problems, the course includes machine learning topics in data science and data preprocessing. To convert the source codes into meaningful pieces of softwares, the course also covers topics in building GUI's using GUIDE and App Designer utilities of matlab.

Machine learning (ML) is a type of programming that enables computers to automatically learn from data provided to them and improve from experience without deliberately being programmed. It is based on algorithms that parse data, learn and analyze them, and make predictions or intelligent decisions in an autonomous fashion. With this clever characterization of Machine Learning, it is often interchanged with Artificial Intelligence (AI). However, to be accurate, ML is only a subset of artificial intelligence. Machine Learning is simply applied AI based on the idea that machines need to be given access to data in order for them learn and analyze it themselves.