If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Editor's note: This tutorial was originally published as course instructional material, and may contain out-of-context references to other courses therein; this takes nothing away from the validity or usefulness of the material. This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. Much of the work involved in analyzing data resides in importing, cleaning and transforming data in preparation for analysis. Therefore, the first half of the course is comprised of a 2-part overview of basic and intermediate Pandas usage that will show how to effectively manipulate datasets in memory. This includes tasks like indexing, alignment, join/merge methods, date/time types, and handling of missing data.
Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language. The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries.
I created this course to take you by hand and teach you all the concepts, and take your statistical modeling from basic to an advanced level for practical data analysis. Frankly, this is the only one course you need to complete in order to get a head start in practical statistical modeling for data analysis using R. My course has 9.5 hours of lectures and provides a robust foundation to carry out PRACTICAL, real-life statistical data analysis tasks in R, one of the most popular and FREE data analysis frameworks. This course is your sure-fire way of acquiring the knowledge and statistical data analysis skills that I acquired from the rigorous training I received at 2 of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One. To be more specific, here's what the course will do for you: The course will mostly focus on helping you implement different statistical analysis techniques on your data and interpret the results.
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. The course also covers normality tests, test of homogeneity, and multiple comparison tests. After attending this course you would be able to create SPSS file, define variables, enter data, run descriptive statistics, create graphs, find out relationship between variables, test the linearity and normality of the data, run hypothesis testing and interpret the results.
This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas. For students familiar with Git, you may simply clone this repository to obtain all the materials (iPython notebooks and data) for the tutorial. He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis.