Participants were given a mall as a context and then use machine learning to simulate an aspect of the mall. The idea in the workshop is to open both tech and non-technical people for understanding and apply machine learning.

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.

This course is all about A/B testing. A/B testing is used everywhere. A/B testing is all about comparing things. If you're a data scientist, and you want to tell the rest of the company, "logo A is better than logo B", well you can't just say that without proving it using numbers and statistics. Traditional A/B testing has been around for a long time, and it's full of approximations and confusing definitions. In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.

With so many R based Data Science & Machine Learning courses around, why this course? This means, this course covers MAIN ASPECTS of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science. In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge –and boost your career to the next level.

Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes over 80 lectures spanning 12 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I'll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn't. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon.

Get your team access to Udemy's top 2,500 courses anytime, anywhere. Cancer Genomics Neural Networks vs k-NN Classifiers: Machine Learning for Python Hackers is a crash course in Data Science and Cancer Genomics for anyone interested in cancer research. The course starts out with loading up a cancer dataset to split train and test. This course is unique in Data Science in that it uses the mglearn library for better visualization and is dedicated to providing details as such so the student can follow along with no ambiguity.

The Finnish Center for Artificial Intelligence now offers a free six-part online course, The Elements of AI, available to anyone. I like free stuff: Especially when there's no catch. Sign up is quick and simple, and there's no application process. Completion of the course will earn you a LinkedIn certificate and, if you're enrolled in a Finnish university, you can get a couple credits. It's happening, Join 15k digital minds to shape what's next for your business More importantly, you'll get a free education designed to introduce students to the basic concepts surrounding artificial intelligence, machine learning, and deep learning.

This course will introduce you to some of the most widely used predictive modeling techniques and their core principles. Models such as multiple linear regression, logistic regression, auto-regressive integrated moving average (ARIMA), decision trees, and neural networks are frequently used in solving predictive analytics problems.

Get your team access to Udemy's top 2,500 courses anytime, anywhere. However I tells you all about software you should install for machine learning & neural networks. Hope that serves you well. What is machine learning / ai? How to lean machine learning in practice?

This module is part of the Innovation Accelerators section of the Digital Business Global Master Program. Artificial intelligence (AI) is going to be a disruptive force in business and society. We can see the technologies playing out in the marketplace already. And those businesses that have data, software competencies and the vision and means to make the necessary investments are leading the way. This module will present why this is happening the developing technologies and the business dynamics and explore how businesses are capitalizing on this emerging force.