It is almost certain that the sub-field of machine learning/artificial intelligence has progressively gained more fame in recent years. As Big Data is in fashion in the tech industry right now, machine learning is staggeringly effective to make predictions or computed recommendations with lot of information. Probably the most well-known cases of machine learning are Netflix or Amazon's algorithms. Machine learning is a type of artificial intelligence (AI) that enables programming applications to be exact in anticipating results without being explicitly modified. The fundamental preface of machine learning is to build algorithms that can get input information and utilize statistical analysis to predict an output value within a worthy range.

Get your team access to Udemy's top 2,500 courses anytime, anywhere. This course is a workshop on logistic regression using R.

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

Control of Mobile Robots is a course that focuses on the application of modern control theory to the problem of making robots move around in safe and effective ways. The structure of this class is somewhat unusual since it involves many moving parts - to do robotics right, one has to go from basic theory all the way to an actual robot moving around in the real world, which is the challenge we have set out to address through the different pieces in the course.

Being a high schooler myself and having studied Machine Learning and Artificial Intelligence for a year now, I believe that there fails to exist a learning path in this field for High School students. This is my attempt to create one. Over the past few months, I've tried to spend a couple of hours every day understanding this field, be it watching Youtube videos or undertaking projects. I've been guided by older peers who've had far more experience than me, and now feel that I have ample experience to share my insights. All the information that I have compiled in this guide is intended for high schoolers wishing to excel in this up and coming field.

The new MITx MicroMasters Program in Statistics and Data Science, which opened for enrollment today, will help online learners develop their skills in the booming field of data science. The program offers learners an MIT-quality, professional credential, while also providing an academic pathway to pursue a PhD at MIT or a master's degree elsewhere. "There are many online programs that provide a professional overview of data science, but they don't offer the level of detail learners gain from an actual, residential master's program," says Professor Devavrat Shah, faculty director of the program and MIT professor in the Department of Electrical Engineering and Computer Science (EECS). "This new MicroMasters program in Statistics and Data Science is bringing the quality, rigor, and structure of a master's-level, residential program in data science at MIT to a wider audience around the world, and at a very accessible price, so people can learn anywhere they are while keeping their day jobs." In all, seven universities will be accepting the new MicroMasters Statistics and Data Science (SDS) credential towards a master's degree, including the Rochester Institute of Technology (United States), Doane University (United States), Galileo University (Guatemala), Reykjavik University (Iceland), Curtin University (Australia), Deakin University (Australia), and RMIT University (Australia).

Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible. This team has decades of practical experience in working with Java and with billions of rows of data. Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst.

About this course: The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

A staggering amount of data is created everyday; analyzing and organizing this enormous amount of data can be quite a complex task. Haskell is a powerful and well-designed functional programming language that is designed to work with complex data. It is trending in the field of data science as it provides a powerful platform for robust data science practices. This course will introduce the basic concepts of Haskell and move on to discuss how Haskell can be used to solve the issues by using the real-world data. The course will guide you through the installation procedure, after you have all the tools that you require in place, you will explore the basic concepts of Haskell including the functions, and the data structures.

About this course: This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to.