About this course: We'll implement (in Python) together efficient programs for a problem needed by delivery companies all over the world millions times per day -- the travelling salesman problem. The goal in this problem is to visit all the given places as quickly as possible. How to find an optimal solution to this problem quickly? We still don't have provably efficient algorithms for this difficult computational problem and this is the essence of the P versus NP problem, the most important open question in Computer Science. Still, we'll implement several solutions for real world instances of the travelling salesman problem.

Are you looking forward to learning the basics of Machine Learning and develop high performing apps? Then, this Learning Path is perfect for you. The question frequently asked by developers across the globe is, "How do I get started in Machine Learning?" One reason could be the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This systematic guide will teach you various Machine Learning techniques as well as Neural Networks in Machine Learning.

Course updated 12/20/2017 - Fully up-to-date for all sections! Projects in Programming Languages with Ruby, Java and Python is an in-depth and comprehensive introduction to project based programming using 3 of the most popular and financially rewarding programming languages out there - Ruby, Java and Python. Some of the most popular web app frameworks in the world today like Ruby on Rails, Django, Flask are based on these languages. This is the course you have been waiting for, a one-stop-shop for everything programming that makes it easy to get started and keeps your attention while you work your way through fun and interesting projects based on real-life problems including Object Oriented Programming! You'll find learning both quick and fun and if you are not satisfied - I offer a full money back guarantee, as long as you make your request within 30 days of your purchase of the course.

Get your team access to Udemy's top 2,500 courses anytime, anywhere. If you have started using Python, by now you must have come to know the simplicity of the language. This course is designed to help you get more comfortable with programming in Python. It covers completely, the concept of linked list using Python as the primary language. You need to be equipped with the basics of Python such as variables, lists, dictionary and so on.

It addresses all manufacturing organizations, from OEMs through their supply chains, to small independent producers. It is mostly focused on, but not limited to, creating mechanical parts. It is intricately linked with the Sketcher Workbench.

This video course is a practical guide for developers who want to get started with building computer vision applications using Python 3. The video is divided into six sections: Throughout this video course, three image processing libraries: Pillow, Scikit-Image, and OpenCV are used to implement different computer vision algorithms. The course will help you build Computer Vision applications that are capable of working in real-world scenarios effectively. Some of the applications that we look at in the course are Optical Character Recognition, Object Tracking and building a Computer Vision as a Service platform that works over the internet. Saurabh Kapur is a computer science student at Indraprastha Institute of Information Technology, Delhi. His interests are in computer vision, numerical analysis, and algorithm design.

Python features numerous numerical and mathematical toolkits such as: Numpy, Scipy, Scikit learn and SciKit, all used for data analysis and machine learning. With the aid of all of these, Python has become the language of choice for data scientists for data analysis, visualization, and machine learning. This video aims to teach Python developers how to perform data analysis with the language by taking advantage of the core data science libraries in the Python ecosystem. The learning objective for viewers is to understand how to locate, manipulate, and analyse data with Python, with the ability to analyse large and small sets of data using libraries such as Numpy, pandas, IPython and SciPy. This is a two part series.

Systems administration is the field of IT that's responsible for maintaining reliable computers systems in a multi-user environment. In this course, you'll learn about the infrastructure services that keep all organizations, big and small, up and running. You'll learn how to manage and configure servers, how to use industry tools to manage computers, user information, and user productivity. Finally, you'll learn how to recover your organization's IT infrastructure in the event of a disaster. By the end of this course you'll be able to: - utilize best practices for choosing hardware, vendors, and services for your organization.

This course is about data structures and algorithms. We are going to implement the problems in Python, but I try to do it as generic as possible: so the core of the algorithms can be used in C or Java. I highly recommend typing out these data structures and algorithms several times on your own in order to get a good grasp of it. In the first part of the course we are going to learn about basic data structures such as linked lists, stacks, queues, binary search trees, heaps and some advanced ones such as AVL trees and red-black trees.. The second part will be about graph algorithms such as spanning trees, shortest path algorithms and graph traversing.

Get your team access to Udemy's top 2,500 courses anytime, anywhere. There are lot of interview Questions out there.