Udemy Coupon - Solve Real World Business Problems with AI Solutions Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team English [Auto-generated], French [Auto-generated], 5 more Students also bought Artificial Intelligence: Reinforcement Learning in Python Data Science: Natural Language Processing (NLP) in Python Recommender Systems and Deep Learning in Python Cluster Analysis and Unsupervised Machine Learning in Python Natural Language Processing with Deep Learning in Python Preview this Course GET COUPON CODE Description Structure of the course: Part 1 - Optimizing Business Processes Case Study: Optimizing the Flows in an E-Commerce Warehouse AI Solution: Q-Learning Part 2 - Minimizing Costs Case Study: Minimizing the Costs in Energy Consumption of a Data Center AI Solution: Deep Q-Learning Part 3 - Maximizing Revenues Case Study: Maximizing Revenue of an Online Retail Business AI Solution: Thompson Sampling Real World Business Applications: With Artificial Intelligence, you can do three main things for any business: Optimize Business Processes Minimize Costs Maximize Revenues We will show you exactly how to succeed these applications, through Real World Business case studies. And for each of these applications we will build a separate AI to solve the challenge. In Part 1 - Optimizing Processes, we will build an AI that will optimize the flows in an E-Commerce warehouse. In Part 2 - Minimizing Costs, we will build a more advanced AI that will minimize the costs in energy consumption of a data center by more than 50%! Just as Google did last year thanks to DeepMind.

Create a computer vision program with python and OpenCV.

Getting Started with Raspberry Pi even if you are a beginner, Deep Learning Basics, Object Detection Models - Pros and Cons of each CNN, Setup and Install Movidius Neural Compute Stick (NCS) SDK, Currently, the OpenVINO is available for Raspbian, so the NCS2 is already compatible with the Raspberry Pi, but this course is mainly for the Movidius (NCS version 1).

Introduction to 50 Must know Topics of Machine Learning, Data science. Introduction to must know concepts in Machine Learning which will help you to prepare for interview. You will get an idea of complete syllabus in Machine Learning. This course is designed to give you introduction to syllabus of machine learning. If you want to get started with machine learning then this course will help you.

Online Courses Udemy - Machine Learning A-Z, Data Science, Python for Machine Learning, Math for Machine Learning, Statistics for Data Science Created by Jitesh Khurkhuriya, Jitesh's Data Science & Machine Learning A-Z Team English Students also bought Machine Learning, Data Science and Deep Learning with Python Intro to Data Science: Your Step-by-Step Guide To Starting Introduction to Machine Learning for Data Science Generate and visualize data in Python and MATLAB Statistics for Data Science and Business Analysis Preview this course GET COUPON CODE Description Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning A-Z, Deep learning and more? Well, you have come to the right place. This Data Science and Machine Learning course has 250 lectures, more than 25 hours of content, 11 projects including one Kaggle competition with top 1 percentile score, code templates and various quizzes. Today Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others.

Are you ready to kickstart your Advanced NLP course? Are you ready to deploy your machine learning models in production at AWS? You will learn each and every steps on how to build and deploy your ML model on a robust and secure server at AWS. Prior knowledge of python and Data Science is assumed. If you are AN absolute beginner in Data Science, please do not take this course. This course is made for medium or advanced level of Data Scientist.

Are you looking to land a top-paying job in Data Science? Or are you a seasoned AI practitioner who want to take your career to the next level? Or are you an aspiring entrepreneur who wants to maximize business revenue with Data Science and Artificial Intelligence? If the answer is yes to any of these questions, then this course is for you! Data Science is one of the hottest tech fields to be in right now!

Live Online Workshop During the workshop, you'll learn the basics of data science using Python programming through examples provided by the instructor. No prior programming experience is necessary. You must join the webinar via a computer. In this live online workshop, you will learn the basics of machine learning. Participants will learn how to apply machine learning to predict customer loan default probability through an instructor-led live demo.

This Specialization will master you in fundamentals of probabilistic graphical models. Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains.

Spiking neuron networks have been used successfully to solve simple reinforcement learning tasks with continuous action set applying learning rules based on spike-timing-dependent plasticity (STDP). However, most of these models cannot be applied to reinforcement learning tasks with discrete action set since they assume that the selected action is a deterministic function of firing rate of neurons, which is continuous. In this paper, we propose a new STDP-based learning rule for spiking neuron networks which contains feedback modulation. We show that the STDP-based learning rule can be used to solve reinforcement learning tasks with discrete action set at a speed similar to standard reinforcement learning algorithms when applied to the CartPole and LunarLander tasks. Moreover, we demonstrate that the agent is unable to solve these tasks if feedback modulation is omitted from the learning rule. We conclude that feedback modulation allows better credit assignment when only the units contributing to the executed action and TD error participate in learning.