About this course: How can we use computers to create expressive, compelling music? And how can we write computer software to help us create and organize sounds in new ways? This course provides a hands-on introduction to the field of music technology as both a creative musical practice and an interdisciplinary technical research pursuit. Students will be able to compose music in digital audio workstation software using both audio and symbolic representations; to write code to algorithmically generate music, analyze sound, and design sound; and to describe the essential theory and history behind these activities as well as their connection to cutting-edge computer music research. Through the exploration of topics such as acoustics, psychoacoustics, digital sound, digital signal processing, audio synthesis, spectral analysis, algorithmic composition, and music information retrieval, we will explore the deep relationships between art and science, between theory and practice, and between experimental and popular electronic music.

A high-level course of AI to learn how Machine Learning provides the foundation for AI, and how you can leverage cognitive services in your apps. Artificial Intelligence will define the next generation of software solutions. This computer science course provides an overview of AI, and explains how it can be used to build smart apps that help organizations be more efficient and enrich people's lives. It uses a mix of engaging lectures and hands-on activities to help you take your first steps in the exciting field of AI. Discover how machine learning can be used to build predictive models for AI.

This course is designed to make you an expert in AWS Machine Learning and it teaches you how to convert your cool ideas into highly scalable products in a matter of days. Biggest challenge for a Data Science professional is how to convert the proof-of-concept models into actual products that your customers can use. There are several courses on machine learning that teach you how to build models in R, Python, Matlab and so forth. However, converting a model into a scalable solution and integrating with your existing application requires a lot of effort and development. The real success of your ideas and concepts depends on how soon you can put the capabilities in the hands of your customers.

With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering.

The demand for Data Scientists is immense. In this course, you'll learn how you can play a part in fulfilling this demand and build a long, successful career for yourself. The #1 goal of this course is clear: give you all the skills you need to be a Data Scientist who could start the job tomorrow... within 6 weeks. With so much ground to cover, we've stripped out the fluff and geared the lessons to focus 100% on preparing you as a Data Scientist. You'll pick up all the core concepts that veteran Data Scientists understand intimately.

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!

Petko Aleksandrov is professional trader and mentor at EA Forex Academy. He teaches algorithmic trading in his courses and shares his trading strategies. You will learn how he creates 100s of Expert Advisors – Robots for trading. You will receive included 99 Robots for Ethereum trading. You will see how he tested the strategies for one month period with just few clicks.

One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. The course will teach you those fundamental concepts of machine learning by implementing practical exercises which are based on live examples.

About this course: We all learn numbers from the childhood. Some of us like to count, others hate it, but any person uses numbers everyday to buy things, pay for services, estimated time and necessary resources. People have been wondering about numbers' properties for thousands of years. And for thousands of years it was more or less just a game that was only interesting for pure mathematicians. Famous 20th century mathematician G.H. Hardy once said "The Theory of Numbers has always been regarded as one of the most obviously useless branches of Pure Mathematics".

This module, along with module 2B introduces two key concepts in statistics/epidemiology, confounding and effect modification. A relation between an outcome and exposure of interested can be confounded if a another variable (or variables) is associated with both the outcome and the exposure. In such cases the crude outcome/exposure associate may over or under-estimate the association of interest. Confounding is an ever-present threat in non-randomized studies, but results of interest can be adjusted for potential confounders.