Myself along with colleagues just published the Cool Vendors in Information Governance and MDM. Data and analytics leaders struggle to leverage data to drive innovation and govern their information assets effectively. New approaches suggest disruptive efforts to drive both innovation and effective governance will change the economics and complexity of innovation.
Application of machine learning in bioinformatics has given rise to a lot of application from diseases prediction, diagnosis and survival analysis. The twin of Bioinformatics, called Computational Biology have emerged largely into development of softwares and application using machine learning and deep learning techniques for biological image data analysis. Application of machine learning and deep learning in biology need to be explored further for building AI's which can be used for disease diagnosis and prediction. According to the Science Daily news, biologist are increasingly turning into Data Scientist as Bioinformatics Data Scientist or Genomic Data Scientist.
Deep Learning For Coders is a new online course that, for the first time, promises to teach coders how to create state of the art deep learning models. Jeremy says that this is First deep learning course to show end-to-end how to get state of the art results (including how to get a top place in a Kaggle competition) First code-centric full deep learning course (18 hours of lessons) First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet! First deep learning course to show end-to-end how to get state of the art results (including how to get a top place in a Kaggle competition) First code-centric full deep learning course (18 hours of lessons) First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet! First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet!
In this introductory course, the "Backyard Data Scientist" will guide you through wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the "techno sphere around us", why it's important now, and how it will dramatically change our world today and for days to come. We'll then explore the past and the future while touching on the importance, impacts and examples of Machine Learning for Data Science: To make sense of the Machine part of Machine Learning, we'll explore the Machine Learning process: Our final section of the course will prepare you to begin your future journey into Machine Learning for Data Science after the course is complete. So I invite you to join me, the Backyard Data Scientist on an exquisite journey into unlocking the secrets of Machine Learning for Data Science.... for you know - everyday people... like you!
Automation, robotics, algorithms and artificial intelligence (AI) in recent times have shown they can do equal or sometimes even better work than humans who are dermatologists, insurance claims adjusters, lawyers, seismic testers in oil fields, sports journalists and financial reporters, crew members on guided-missile destroyers, hiring managers, psychological testers, retail salespeople, and border patrol agents. A recent study by labor economists found that "one more robot per thousand workers reduces the employment to population ratio by about 0.18-0.34 When Pew Research Center and Elon University's Imagining the Internet Center asked experts in 2014 whether AI and robotics would create more jobs than they would destroy, the verdict was evenly split: 48% of the respondents envisioned a future where more jobs are lost than created, while 52% said more jobs would be created than lost. This survey noted that employment is much higher among jobs that require an average or above-average level of preparation (including education, experience and job training); average or above-average interpersonal, management and communication skills; and higher levels of analytical skills, such as critical thinking and computer skills. A focus on nurturing unique human skills that artificial intelligence (AI) and machines seem unable to replicate: Many of these experts discussed in their responses the human talents they believe machines and automation may not be able to duplicate, noting that these should be the skills developed and nurtured by education and training programs to prepare people to work successfully alongside AI.
We've already learned some classic machine learning models like k-nearest neighbor and decision tree. In this course you'll study ways to combine models like decision trees and logistic regression to build models that can reach much higher accuracies than the base models they are made of. In particular, we will study the Random Forest and AdaBoost algorithms in detail. Since deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.
A few weeks in, I wanted to learn how to actually code machine learning algorithms, so I started a study group with a few of my peers. The most important takeaway from this period was the leap from non-vectorized to vectorized implementations of neural networks, which involved repeating linear algebra from university. So I asked my manager if I could spend some time learning stuff during my work hours as well, which he happily approved. Having gotten a basic understanding of neural networks at this point, I wanted to move on to deep learning.
About this course: Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions.
Our curriculum prepares us for a lifetime career, but a child today can expect to change jobs at least seven times over the course of their lives – and five of those jobs don't exist yet. We have already moved to a data driven economy in the Fourth Industrial Revolution, and this is going to increase even more in the coming years and Machine Learning (ML) and Artificial Intelligence (AI) become business as usual in every aspect of our lives. This can help them to gain knowledge in data analysis at the college level where they will have to learn some kind of a programming language like MATLAB which allows implementation of algorithms. While AI and ML are disrupting jobs, there is great potential to use AI in education.
To be discussed is the use of descriptive analytics (using an unlabeled data set), predictive analytics (using a labeled data set) and social network learning (using a networked data set). He has done extensive research on big data& analytics, fraud detection, marketing analytics and credit risk management. Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research, …) and presented at international top conferences. He is author of the books Credit Risk Management: Basic Concepts, Analytics in a Big Data World, Fraud Analytics using Descriptive, Predictive and Social Network Techniques, and Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS.