About this course: By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We'll learn about the fundamentals of Linear Algebra and Neural Networks. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras one real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases.

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models.

The value of a confidence interval is its ability to quantify the uncertainty of the estimate. It provides both a lower and upper bound and a likelihood. Taken as a radius measure alone, the confidence interval is often referred to as the margin of error and may be used to graphically depict the uncertainty of an estimate on graphs through the use of error bars. Often, the larger the sample from which the estimate was drawn, the more precise the estimate and the smaller (better) the confidence interval. We can also say that the CI tells us how precise our estimate is likely to be, and the margin of error is our measure of precision.

I want to offer a few considerations about what I consider the inevitable transformation of education by artificial intelligence, but to do so, I'm going to first invite you into my childhood and early college years for a moment. It might not seem related to AI, but if you bear with me, I promise to offer you a few important and incredibly relevant considerations, as well as an important challenge and invitation. Mr. Bently was an extraordinary teacher. Many others faced far greater challenges to be sure, but suffice it to say that when I went to school, it was not easy to set aside worries and concerns from outside of school enough to get the most out of what happened in most of my classes. Nonetheless, when I walked up to the room to enter Mr. Bentley's class, he consistently greeted me and every other student at the door. As he wished us each a good morning, he also paid attention to the little things and deliberately said something that made each of us keenly aware that he cared about us and noticed us.

The way we help organisations and engineers keep pace with technological advancement and a growing learning deficit is by offering technical workshops & consulting services led by our community of Machine Learning & Deep Learning experts who share their knowledge and skills with the next generation of experts. We also can identify key areas where AI solutions will have maximum impact on your business through educational workshops and strategic roadmap planning.

To help you stay up to date on online training opportunities, we're releasing a monthly list of the latest free Data and Artificial Intelligence (AI) sessions in one convenient post. Azure SQL Database is the intelligent, fully managed relational cloud database service that provides the broadest SQL Server engine compatibility, so you can migrate your SQL Server databases without changing your apps. Accelerate app development and make maintenance easy and productive using the SQL tools you love to use. Here's a rundown of recent and upcoming training sessions to help you learn more. Getting ahead means embracing digital transformation to leverage the cloud.

A lab excercise is show cased to calculate ROC and AUC for a sample data set of logistic regression model. Learn and apply the practical code to test the data. Data Scientists take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics, and programming to clean, massage and organize. But worry not we are here to the rescue and teach you how to be a data scientist, more importantly, upgrade your analytic skills to tackle any problem in the field of data science. Join us on "statinfer.com" for becoming a "scientist in data science" Our "Machine Learning" course is now available on Udemy https://www.udemy.com/machine-learnin... Part 1 – Introduction to R Programming.

Following the launch (May 16th) of the FIBR report, "Artificial Intelligence: Practical Superpowers," we are hosting a follow-up webinar to present the paper and its insights. As one of the first reports that looks at AI applied to financial services in Africa, this webinar is for fintech companies and FSPs in Africa, that might be interested or looking to move into AI. By presenting insights from the report and having a panel discussion, we seek to create awareness around the real-use cases of AI for FSPs that can augment the ability of companies to do business better. Panelists: Matt Grasser, Deputy Director of Inclusive Fintech BFA Qiuyan Xu, Chief Data Scientist Cignifi Andrea Ottina, Chief Business Development Officer Access Tanzania Sheel Mohot, Partner 500 startups Moderated by Jane del Ser, Insights & Influence at BFA Related content: Read the report - http://bfa.works/ai-launch An Experimental Gallery of AI Applications for MSMEs and PAYGo - FIBR.AI FIBR stands for Financial Inclusion on Business Runways and aims to learn how to transform emerging business data about low-income individuals and link them to inclusive financial services to deepen financial inclusion and its impact.

The history of data science, machine learning, and artificial Intelligence is long, but it's only recently that technology companies - both start-ups and tech giants across the globe have begun to get excited about it… Why? With the arrival of cloud computing and multi-core machines - we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data. This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way.

We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands for real-time control. The Grounded Semantic Mapping Network (GSMN) is a fully-differentiable neural network architecture that builds an explicit semantic map in the world reference frame by incorporating a pinhole camera projection model within the network. The information stored in the map is learned from experience, while the local-to-world transformation is computed explicitly. We train the model using DAggerFM, a modified variant of DAgger that trades tabular convergence guarantees for improved training speed and memory use. We test GSMN in virtual environments on a realistic quadcopter simulator and show that incorporating an explicit mapping and grounding modules allows GSMN to outperform strong neural baselines and almost reach an expert policy performance. Finally, we analyze the learned map representations and show that using an explicit map leads to an interpretable instruction-following model.