There are many problems associated with analyzing data sets that contain missing data. However, there are various techniques to'fill in,' or impute, missing data values with reasonable estimates based on the characteristics of the data itself and on the patterns of'missingness.' Generally, techniques appropriate for imputing missing values in multivariate normal data and not as useful when applied to non-multivariate-normal data. This Visualization and Imputation of Missing Data course focuses on understanding patterns of'missingness' in a data sample, especially non-multivariate-normal data sets, and teaches one to use various appropriate imputation techniques to "fill in" the missing data. Using the VIM and VIMGUI packages in R, the course also teaches how to create dozens of different and unique visualizations to better understand existing patterns of both the missing and imputed data in your samples.

We will walk you through all the aspects of machine learning from simple linear regressions to the latest neural networks, and you will learn not only how to use them but also how to build them from scratch. Big part of this path is oriented on Computer Vision(CV), because it's the fastest way to get general knowledge, and the experience from CV can be simply transferred to any ML area. We will use TensorFlow as a ML framework, as it is the most promising and production ready. Learning will be better if you work on theoretical and practical materials at the same time to get practical experience on the learned material. Also if you want to compete with other people solving real life problems I would recommend you to register on Kaggle, as it could be a good addition to your resume.

Are you interested in understanding machine learning concepts and building real-time projects with R, but don't know where to start? Then, this is the perfect course for you! The aim of machine learning is to uncover hidden patterns, unknown correlations, and find useful information from data. In addition to this, through incorporation with data analysis, machine learning can be used to perform predictive analysis. With machine learning, the analysis of business operations and processes is not limited to human scale thinking; machine scale analysis enables businesses to capture hidden values in big data.

This course explores a variety of machine learning and data science techniques using real life datasets/images/audio collected from several sources. These realistic situations are much better than dummy examples, because they force the student to better think the problem, pre-process the data in a better way, and evaluate the performance of the prediction in different ways. The datasets used here are from different sources such as Kaggle, US Data.gov, And each lecture shows how to preprocess the data, model it using an appropriate technique, and compute how well each technique is working on that specific problem. Certain lectures contain also multiple techniques, and we discuss which technique is outperforming the other.

LONDON: Artificial intelligence (AI) will become the primary way banks interact with their customers within the next three years, according to three quarters of bankers surveyed by consultancy Accenture in a new report. Four in five bankers believe AI will "revolutionise" the way in which banks gather information as well as how they interact with their clients, said the Accenture Banking Technology Vision 2017 report, which surveyed more than 600 top bankers and also consulted tech industry experts and academics. Artificial intelligence -- the technology behind driverless cars, drones and voice-recognition software -- is seen by the financial world as a key technology which, along with other "fintech" innovations such as blockchain, will change the face of banking in the coming years. More than three-quarters of respondents to the survey believed that AI would enable more simple user interfaces, which would help banks create a more human-like customer experience. "The big paradox here is that people think technology will lead to banking becoming more and more automated and less and less personalised, but what we've seen coming through here is the view that technology will actually help banking become a lot more personalised," said Alan McIntyre, head of the Accenture's banking practice and co-author of the report. "(It) will give people the impression that the bank knows them a lot better, and in many ways it will take banking back to the feeling that people had when there were more human interactions."

If you have and you want to learn the science behind them, you have come to the right place. In this course, I will show you how these companies use Recommender systems or Machine Learning to influence your purchasing decisions. This course is timely and extremely relevant now as almost all major service-oriented companies function on recommender systems. You will understand how these systems work and learn how to build and use your own recommender systems, just like these big companies do. Learn how to build the recommender systems that are being used by almost every big service-oriented company in today's world with this introductory course for beginners.

This course explores several modern machine learning and data science techniques in R. As you probably know, R is one of the most used tools among data scientists. Most of the examples presented in this course come from real datasets collected from the web such as Kaggle, the US Census Bureau, etc. All the lectures can be downloaded and come with the corresponding material. The teaching approach is to briefly introduce each technique, and focus on the computational aspect. The mathematical formulas are avoided as much as possible, so as to concentrate on the practical implementations.

Are you looking to gain in-depth knowledge of machine learning and deep learning? Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. R is one of the leading technologies in the field of data science. Starting out at a basic level, this Learning Path will teach you how to develop and implement machine learning and deep learning algorithms using R in real-world scenarios. The Learning Path begins with covering some basic concepts of R to refresh your knowledge of R before we deep-dive into the advanced techniques.

IBM SPSS Statistics is most widely used statistical analysis software in social sciences and business research. From simple statistical analyses like descriptive statistics, graphs, cross tabulation, correlation, regression analysis to hypothesis testing techniques like t-test, chi-square, ANOVA, and multivariate analysis like factor analysis, cluster analysis, conjoint analysis, Multiple ANOVA, Multiple Regression, Hierarchical Linear Models can be calculated with few clicks. At the same time tests of normality like K-S test, Shapiro-Wilk test, Levene's Test of Homogeneity of Variances, Fishers Least Significant Difference (LSD) test, Cronbach's scale reliability and many other complex statistical techniques can be calculated with ease. In this course we cover, univariate, Bivariate statistical techniques and hypothesis testing tools like Chi-Square, one sample t-test, paired t-test, independent t-test, and ANOVA. The course also covers normality tests, test of homogeneity, and multiple comparison tests.

Machine Learning is currently one of the hottest topics out there. The working place of tomorrow is related to ML. No wonder that interest has drastically risen. The difficult question for beginners is how to get into it. From my personal experience the best way is to get one's hands dirty and apply machine learning in practice.