The objective of this course is to give you a wholistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved.

It easier than ever before to train a neural network. However, it is rarely the case that you can just take code from a tutorial and directly make it work for your application. Interestingly, many of the most important tweaks are barely discussed in the academic literature but at the same time critical to make your product work. Therefore I thought it would be helpful for other people who plan to use deep learning in their business to understand some of these tweaks and tricks. This post is based on my talk I gave on May 10 at the Berlin.AI meetup (the slides are here).

Includes key concepts of statistical analysis - Probability theory, Types of distribution, Central limit theorem, Hypothesis testing, Statsistical inference.

Neural networks provide the possibility to solve complicated non linear problems. They can be used in various areas such as signal classification, forecasting timeseries and pattern recognition. A neural network is a model inspired by the human brain and consists of multiple connected neurons. For getting a deeper understanding, I recommend checking out Neural Networks and Deep Learning. Within the last years, multiple Javascript frameworks were developed that can help you to create, train and use Neural Networks for different purposes.

Upcoming Webinar To be Presented on June 2, 2017 at 12PM ET Webinar Topics: Customer Experience, Customer Relationship A decade of aggressive mass promotions is contributing to lower customer engagement rates, reducing ROI to less than 1% and leading to high unsubscribe rates. It is time to make the change towards optimizing promotional strategies with better customer insights. Join Forrester's Senior Analyst Brandon Purcell, Charming Charlie's Ujwal Dhoot, VP – Marketing & eCommerce, and Manthan's Varij Saurabh, Director, Customer Analytics as they provide insights into how AI will impact marketing and how it can help marketers solve key customer marketing challenges. In this webinar, you'll learn about: • How AI impacts marketing? FEATURED PANELISTS: Brandon Purcell, Senior Analyst, Forrester Brandon serves Customer Insights Professionals, covering customer analytics and artificial intelligence.

What if machines could learn? This has been one of the most intriguing questions in science fiction and philosophy since the advent of machines. With modern technology such questions are no longer bound to creative conjecture, machine learning is all around us. From deciding which movie you might want to watch next on Netflix to predicting stock market trends, machine learning has a profound impact on how data is understood in the modern era. This tutorial aims to give an accessible introduction to how to use machine learning techniques for your own projects and datasets.

"Speaker: Christopher Fonnesbeck This intermediate-level tutorial will provide students with hands-on experience applying practical statistical modeling methods on real data. Unlike many introductory statistics courses, we will not be applying ""cookbook"" methods that are easy to teach, but often inapplicable; instead, we will learn some foundational statistical methods that can be applied generally to a wide variety of problems: maximum likelihood, bootstrapping, linear regression, and other modern techniques. The tutorial will start with a short introduction on data manipulation and cleaning using [pandas](http://pandas.pydata.org/), Slightly more advanced topics include bootstrapping (for estimating uncertainty around estimates) and flexible linear regression methods using Bayesian methods. By using and modifying hand-coded implementations of these techniques, students will gain an understanding of how each method works.

Jeremy P. Howard, @JeremyPHoward, is a leading Machine Learning and Deep learning researcher and entrepreneur. His current startup is fast.ai Previously, he was CEO and founder of Enlitic, Kaggle President, and #1 ranked Kaggle competitor. Jeremy initiatives attracts a lot of attention in the industry, so I was very interested to learn from him about his latest project, a first Deep Learning for coders MOOC at course.fast.ai. The course is totally free and includes no advertising - Jeremy created it purely as a service to the community.

For decades robotic exoskeletons were the subject of science fiction novels and movies. But in recent years, exoskeleton technology has made huge progress towards reality and exciting research projects and new companies have surfaced. Typical applications of today's exoskeletons are stroke therapy or support of users with a spinal cord injury, or industrial applications, such as back support for heavy lifting or power tool operation. And while the field is growing quickly, it is currently not easy to get involved. Learning materials or exoskeleton courses or classes are not widely available yet. This has made it difficult, as learning about exoskeletons is not possible by theory alone, but ideally, involves practical hands-on experience (feel it understand it).

In this end-to-end Python machine learning tutorial, you'll learn how to use Scikit-Learn to build and tune a supervised learning model! We'll be training and tuning a random forest for wine quality (as judged by wine snobs experts) based on traits like acidity, residual sugar, and alcohol concentration. Before we start, we should state that this guide is meant for beginners who are interested in applied machine learning. Our goal is introduce you to one of the most flexible and useful libraries for machine learning in Python. We'll skip the theory and math in this tutorial, but we'll still recommend great resources for learning those. To move quickly, we'll assume you have this background.