The classification of complex data usually requires the composition of processing steps. Here, a major challenge is the selection of optimal algorithms for preprocessing and classification (including parameterizations). Nowadays, parts of the optimization process are automized but expert knowledge and manual work are still required. We present three steps to face this process and ease the optimization. Namely, we take a theoretical view on classical classifiers, provide an approach to interpret the classifier together with the preprocessing, and integrate both into one framework which enables a semiautomatic optimization of the processing chain and which interfaces numerous algorithms.

This guide to meant to help web developers, software engineers and other IT industry people to transition into analytics / data science industry. Last week, I was taking a guest lecture with one of the well known institutes in India. Rather (un)surprisingly, more than 60% of the students comprised of experienced IT Professionals. Most of them are facing a common problem, "I have been in IT / software / web development for more than a few years and want to up-skill myself in analytics. I have taken a few MOOCs and have tried using a few books / platforms. Still, I don't get it what should I do next?"

This post was written by Ofir Press. Ofir is a graduate student at Tel-Aviv University's Deep Learning Lab. His main focus is on using deep learning for natural language processing. "Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online. If you don't have the relevant math background: There is an incredible amount of free material online that can be used to learn the required math knowledge. Gilbert Strang's course on linear algebra is a great introduction to the field. For the other subjects, edX has courses from MIT on both calculus and probability. If you are interested in learning more about machine learning: Andrew Ng's Coursera class is a popular choice as a first class in machine learning. There are other great options available such as Yaser Abu-Mostafa's machine learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners. Knowledge in machine learning isn't really a prerequisite to learning deep learning, but it does help. In addition, learning classical machine learning and not only deep learning is important because it provides a theoretical background and because deep learning isn't always the correct solution. Geoffrey Hinton's Coursera class "Neural Networks for Machine Learn... covers a lot of different topics, and so does Hugo Larochelle's "Neural Networks Class".

Democratization of content: In 2018, we will see more professionals outside of the training team contributing in creation of content. The concept of crowd sourcing will evolve in a big way with solution architects, professional services, and support organizations creating the content. Customers and partners will also contribute content as they implement the cloud computing solutions and identify new use cases and design and implementation best practices. This trend will see the role of training organizations evolving. Training teams will act more as a strategy and content curation arm providing tools and templates to the subject matter experts to develop the content and then curating the content.

This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Check out the entire series here: https://www.youtube.com/playlist?list... Support me! http://www.patreon.com/calebcurry

About the course - Cluster analysis is one of the most popular techniques used in data mining for marketing needs. The idea behind cluster analysis is to find natural groups within data in such a way that each element in the group is as similar to each other as possible. At the same time, the groups are as dissimilar to other groups as possible. Course materials- The course contains video presentations (power point presentations with voice), pdf, excel work book and sas codes. Course duration- The course should take roughly 10 hours to understand and internalize the concepts.

Ensembles have rapidly become one of the hottest and most popular methods in applied machine learning. Virtually every winning Kaggle solution features them, and many data science pipelines have ensembles in them. Put simply, ensembles combine predictions from different models to generate a final prediction, and the more models we include the better it performs. Better still, because ensembles combine baseline predictions, they perform at least as well as the best baseline model. Ensembles give us a performance boost almost for free! An input array $X$ is fed through two preprocessing pipelines and then to a set of base learners $f {(i)}$. The ensemble combines all base learner predictions into a final prediction array $P$. In this post, we'll take you through the basics of ensembles -- what they are and why they work so well -- and provide a hands-on tutorial for building basic ensembles. To illustrate how ensembles work, we'll use a data set on U.S. political contributions.

This tutorial is an introduction to Artificial Intelligence which explains the need to study AI, AI growth, concept of AI, its use cases and various intelligence types in detail. If you've enjoyed this video, Like us and Subscribe to our channel for more similar informative videos and free tutorials. Got any questions about AI? Ask us in the comment section below. Are you looking for something more? Enroll in our Artificial Intelligence & Deep Learning training course and become a certified AI Expert (https://goo.gl/RdA17B).

About this course: This one-week accelerated on-demand course provides participants a a hands-on introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and productionizing ML models. OBJECTIVES This course teaches participants the following skills: Identify use cases for machine learning Build an ML model using TensorFlow Build scalable, deployable ML models using Cloud ML Know the importance of preprocessing and combining features Incorporate advanced ML concepts into their models Productionize trained ML models PREREQUISITES To get the most of out of this course, participants should have: Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python Familiarity with Machine Learning and/or statistics Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google services are currently unavailable in China).

Our innovative client is creating a new Artificial Intelligence team and is looking to recruit several Scientists with an Artificial Intelligence (AI) / Machine Learning expertise. As our client's Scientist - Artificial Intelligence (AI) / Machine Learning you will; Develop machine learning, artificial intelligence (AI) algorithms to aid product development on a global scale. Have well-documented research experience within a relevant area; Artificial Intelligence (AI) / Machine Learning, image or natural language processing, deep learning. Have programming experience with either Python, SkLearn, Keras and Tensorflow, or similar libraries In return as our client's Scientist - Artificial Intelligence (AI) / Machine Learning you will receive; The opportunity to work in an intellectually stimulating environment where you can see your ideas be put in to practice. An environment that encourages a work/life balance.