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.

Learn python programming with djnago, machine learning, etc. for anyone who wants to become an advanced level programmer. Rise from a beginner to an advanced level programmer in no time! This specialized course is truly meant to make you an advanced level programmer! " It is very good course for the beginners with 0 knowledge as the Teacher is explaining very well and also the course has quizzes and exercises on each and every topic which I loved the most about this course. "amazing this course is nicely and explained in well mannered way not only at the end of the course we will be creating 15 applications using python which is great and also this course can be best for beginners or any aliens must try the explanation is damn good great take a look what are you waiting for:) " Python for Beginners: Learn Python Programming (Python 3) " this is good and its very easy to understand than k U " " The videos are informative and easy to understand.

Created by 365 Careers 7.5 hours on-demand video course Welcome to Time Series Analysis in Python! The big question in taking an online course is what to expect. And we've made sure that you are provided with everything you need to become proficient in time series analysis. We start by exploring the fundamental time series theory to help you understand the modeling that comes afterwards. Then throughout the course, we will work with a number of Python libraries, providing you with a complete training.

CS50's Introduction to Artificial Intelligence with Python is a 7 weeks Short Course program taught at Harvard University, . The program is offered in online modes with part-time options. To successfully obtain CS50's Introduction to Artificial Intelligence with Python from Harvard University you are required to complete 0 credit hours. After completion of CS50's Introduction to Artificial Intelligence with Python you will be able to further continue for advance studies or start career as Web Developer, Software Developer, Python Programmer, Data Scientist, Data Analyst. AI is transforming how we live, work, and play.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. As long as coding and programming are used, algorithms will be at the heart of these technologies, defining what they do and how they do it.

Understand convolution and why it's useful for Deep Learning Understand and explain the architecture of a convolutional neural network (CNN) Implement a CNN in TensorFlow 2 Apply CNNs to challenging Image Recognition tasks Apply CNNs to Natural Language Processing (NLP) for Text Classification (e.g. The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don't exist in the real world! This course will teach you the fundamentals of convolution and why it's useful for deep learning and even NLP (natural language processing). You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself. The basics of machine learning and neurons (just a review to get you warmed up!) Neural networks for classification and regression (just a review to get you warmed up!)

DeepMind is known for leading the way in deep reinforcement learning research. Creating novel agents to conquer the most advanced environments requires the use of some sophisticated infrastructure. In ACME, you'll find everything from deep Q learning all the way up to the R2D2 algorithm. Better yet, it includes all the building blocks to start creating your own custom agents. In this tutorial, I'll show you how to setup ACME and get started making our own deep Q learning and deep deterministic policy gradient agent.

According to Forbes: "60% of the Data Scientist's or Data Analyst's time is spent in cleaning and organising the data..." In this course, you will not just get to know the industry level strategies but also I will practically demonstrate them for better understanding. This course has been practically and carefully designed by industry experts to reflect the real-world scenario of working with messy data. This course will help you learn complex Data Analytic techniques and concepts for easier understanding and data manipulations. We will walk you through step-by-step on each topic explaining each line of code for your understanding. This course aims to help beginners, as well as an intermediate data analyst, students, business analyst, data science, and machine learning enthusiasts, master the foundations of confidently working with data in the real world.

The demand of competent robot assisted surgeons is progressively expanding, because robot-assisted surgery has become progressively more popular due to its clinical advantages. To meet this demand and provide a better surgical education for surgeon, we develop a novel robotic surgery education system by integrating artificial intelligence surgical module and augmented reality visualization. The artificial intelligence incorporates reinforcement leaning to learn from expert demonstration and then generate 3D guidance trajectory, providing surgical context awareness of the complete surgical procedure. The trajectory information is further visualized in stereo viewer in the dVRK along with other information such as text hint, where the user can perceive the 3D guidance and learn the procedure. The proposed system is evaluated through a preliminary experiment on surgical education task peg-transfer, which proves its feasibility and potential as the next generation of robot-assisted surgery education solution.

Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online. In this course, you will learn how to engineer features and build more powerful machine learning models. Who is this course for? So, you've made your first steps into data science, you know the most commonly used prediction models, you perhaps even built a linear regression or a classification tree model. At this stage you're probably starting to encounter some challenges - you realize that your data set is dirty, there are lots of values missing, some variables contain labels instead of numbers, others do not meet the assumptions of the models, and on top of everything you wonder whether this is the right way to code things up.