About this Course 270,294 recent views In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project. This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not. This course is part of multiple programs This course can be applied to multiple Specializations or Professional Certificates programs.

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). 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.

This tutorial presents two essential concepts in data science and automated learning. One is the machine learning pipeline, and the second is its optimization. These two principles are the key to implementing any successful intelligent system based on machine learning. A machine learning pipeline can be created by putting together a sequence of steps involved in training a machine learning model. It can be used to automate a machine learning workflow.

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Created by Dr. Ryan Ahmed, Ph.D., MBA Are you ready to learn python programming fundamentals and directly apply them to solve real world applications in Finance and Banking? If the answer is yes, then welcome to the "The Complete Python and Machine Learning for Financial Analysis" course in which you will learn everything you need to develop practical real-world finance/banking applications in Python! Python is ranked as the number one programming language to learn in 2020, here are 6 reasons you need to learn Python right now! The course is divided into 3 main parts covering python programming fundamentals, financial analysis in Python and AI/ML application in Finance/Banking Industry. In addition, this section will cover key Python libraries for data science such as Numpy and Pandas.

In this hands-on project, we will train a Naive Bayes classifier to predict sentiment from thousands of Twitter tweets. This project could be practically used by any company with social media presence to automatically predict customer's sentiment (i.e.: whether their customers are happy or not). The process could be done automatically without having humans manually review thousands of tweets and customer reviews. Note: This course works best for learners who are based in the North America region.

AWS and Udacity are collaborating to educate developers of all skill levels on machine learning concepts. We invite students 18 years of age or older who are interested in expanding their machine learning skills and expertise to enroll in the AWS Machine Learning Scholarship Program. The goal for this program is to up-level machine learning skills to all, and to cultivate the next generation of ML leaders across the world, with a focus on underrepresented groups. Through its We Power Tech Program, AWS collaborates with professional organizations that are leading initiatives to increase the diversity and talent in technical roles, including organizations like Girls In Tech and the National Society of Black Engineers. The scholarship is open to all for registration starting May 26, 2021, and your learning will begin on June 28, 2021 or July 14, 2021, depending on when your application was submitted.

Free Coupon Discount - Deep Learning: Recurrent Neural Networks in Python, GRU, LSTM, more modern deep learning, machine learning, and data science for sequences Created by Lazy Programmer Inc. English [Auto], Italian [Auto], Preview this Udemy Course GET COUPON CODE Description Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences - but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not - and as a result, they are more expressive, and more powerful than anything we've seen on tasks that we haven't made progress on in decades. So what's going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I'll introduce you to the Simple Recurrent Unit, also known as the Elman unit. We are going to revisit the XOR problem, but we're going to extend it so that it becomes the parity problem - you'll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.

This course is the only course available online that empowers anyone with zero coding and mathematics background to build, train, test and deploy machine learning models at scale. Machine Learning is one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Machine learning is the study of algorithms that teach computers to learn from experience. Through experience (i.e.: more training data), computers can continuously improve their performance.

Calculus is one of the core mathematical concepts in machine learning that permits us to understand the internal workings of different machine learning algorithms. One of the important applications of calculus in machine learning is the gradient descent algorithm, which, in tandem with backpropagation, allows us to train a neural network model. In this tutorial, you will discover the integral role of calculus in machine learning. Calculus in Machine Learning: Why it Works Photo by Hasmik Ghazaryan Olson, some rights reserved. A neural network model, whether shallow or deep, implements a function that maps a set of inputs to expected outputs.