This course will give an overview of all the topics we shall be looking at in this course. We shall begin by describing the oil value chain – the exploration and development, how oil is produced, shipped, and marketed. Moving further, we will learn about the importance of oil in the industry, both as a fuel and as a raw material in various forms in the global economy. Then, we will go through a brief history of oil – how it all began, and the different'kinds' of oil discoverers. We will be introduced to the major players in the oil market – the top producers and the major consumers. We will then see how oil is formed, how it sits deep within the earth and how we discover and refine it. We will learn about the different types of oils, and the methods employed to extract them. This will be followed by a brief overview of the different means of transporting oil, and the risks and benefits associated with the different methods of oil transport. Lastly, we shall look into the different oil benchmarks that prevail globally.

Developing machine learning models is all fun and good, but after developing them you will probably be looking into deploying them into an app to use them. The question is should you put them on the client-side (which is probably the mobile) or should you put the model on a server, send the data to the server and get the results back? In this article, I am going to be discussing the trade-offs and what those 2 methodologies entail. Server-side machine learning models are the most widely-used ML systems simply because the architecture is more simple to implement. For example, you have an application running on the phone (the client) that classifies images.

When it comes to online learning, it's important for colleges to have courses that can adapt to our technology-driven world. That's why our mission at Amesite is to improve the way the world learns, catering to different types of learners while also giving them classes that are transferable. Our courses use AI to give students and instructors an exceptional online learning experience. But why should you use Amesite? Amesite's system uses established social media formats to help learners access content in a familiar setting.

Module 2 Deep Learning with Python Theoretical introduction to Neural Networks for Deep Learning, Python libraries for Deep Learning (Tensorflow), creation of a Neural Network and application to real datasets, Regression applied to different types of structured and unstructured data, such as numerical, categorical, or textual.

Learn the concepts, tools, and techniques to make great maps that answer geographic questions. This Specialization is for those new to mapping and GIS, as well as anyone looking to gain a better understanding of how it all works and why. You will learn practical skills that can be applied to your own work using cutting-edge software created by Esri Inc., the world's leading GIS company and our industry partner. PLEASE NOTE: THE SOFTWARE IS NO LONGER PROVIDED WITH THIS COURSE. The courses are based on the idea that you will get the most out of the learning experience if you can tailor your work to your own interests.

The course is aimed at university-level students of all engineering backgrounds, who would like to learn the basics of modern biomedical engineering, including the development of human-robotic interfaces and systems such as bionic prosthetics. The course is covering the practical basics of almost everything that a modern biomedical engineer is required to know: electronics, control theory, microcontrollers (Arduino), and high-level programming (MATLAB). All covered disciplines do not require any prior knowledge except university-level mathematics and physics. By the end of the course, the students will be able to practically understand and design electronic systems for electrophysiological signal acquisition, connect and program the microcontroller, organise the data transmission between a controller and PC, process the acquired signal and control a simple robot with the acquired signal in real-time. The course is also providing a platform from which the students can improve their skills further by simply adding more complicated systems and experimenting with more advanced control paradigms.

About this Course 14,670 recent views This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions. Upon completing this course you will be able to identify and abstract common data analysis tasks and to encapsulate them in user-facing functions. Because every data science environment encounters unique data challenges, there is always a need to develop custom software specific to your organization's mission. You will also be able to define new data types in R and to develop a universe of functionality specific to those data types to enable cleaner execution of data science tasks and stronger reusability within a team.

A 17-hour AI course is provided by Udemy from Kirill Eremenko, the Founder of SuperDataScience. This class is very beginner friendly, as the course curriculum covers all the basics of AI. In this course, the students will learn the fundamentals of AI, its practical applications, and the theory behind it. Also, they will get to know about models that power AI applications such as self-driving cars. The course also teaches how to solve real-world problems with the help of artificial intelligence.

Indian Space Research Organisation (ISRO) has recently launched a free online course in machine learning, titled as "Machine learning to Deep Learning: A journey for remote sensing data classification". After successful completion of this 5-day course, all the participants will be rewarded with a certificate. However, the course is offered by ISRO, it will be conducted by the Indian Institute of Remote Sensing (IIRS). Anyone from student to working professional is eligible for this free online course and will learn machine learning by experts. The course will start on 5 July 2021 and will end on 9 July 2021.

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! What do you get with Early Bird Access? 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.