Almost all companies these days are investing thousands of dollars in data analysis to get their data analyzed. Well, in fact studies say that there are around 73% of organizations have invested in Big Data. Why do you think that is the case? What can you reap of the data, ideally just 1s and 0s? Moreover, how does this data help an organization's future?

Machine learning and predictive analytics are transforming the way that businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, and is becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data. Its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. This video gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages.

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

Data analysis has recently emerged as a very important focus for a huge range of organizations and businesses. R makes detailed data analysis easier, making advanced data exploration and insight accessible to anyone interested in learning it. This video empowers you by showing you ways to use R to generate professional analysis reports. It provides examples for various important analysis and machine-learning tasks that you can try out with associated and readily available data. You will learn to carry out different tasks on the data to bring it into action.By the end of this course, you will be able to carry out different analyzing techniques, apply classification and regression, and also reduce data.

The Department of Computing Science at the University of Alberta invites applications for tenure-track or tenured faculty positions at all levels. Candidates with a strong research record in the area of Artificial Intelligence (AI), in particular (but not limited to) Machine Learning, Natural Language Processing, Computer Games, Visualization, Security, Planning/Heuristic Search, and Algorithmic Game Theory, will be considered for this position. According to csrankings.org the department is ranked #1 in Canada and averaged #3 in the world in terms of number of publications at top AI venues in the last 10 years, and it is also home to Amii (www.amii.ca), the Alberta Machine Intelligence Institute, formerly known as AICML. It is noteworthy that the 2017 Government of Canada Budget included an investment of $125 million into a Pan-Canadian Artificial Intelligence Strategy which features a major investment in research at the University of Alberta. According to the most recent Times Higher Education World University ranking, the department is ranked 3rd in Canada and 67th in the world.

My son Arthur has just been awarded a prize for story-telling at his primary school. So when I watched that short movie whose script was generated by artificial intelligence, based on thousands of sci-fi books and films, I could certainly see a lot of similarity between both outputs. For me this epitomizes the current state of AI… It is raw, forming, full of potential but still with a long way to go towards maturity. Today we will be talking about the heart in artificial intelligence. After all, if artificial intelligence is, by definition, artificial, how can it have a heart, how can it have emotions?

As bonus, you also learn how to share your analysis results with your collegues friends and others and create visual analysis of your results. You will also have access to some practice questions, which will give you hand on experience.

R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This video course will take you from very basics of R to creating insightful machine learning models with R. You will start with setting up the environment and then perform data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationship. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction. Yu-Wei, Chiu (David Chiu) is the founder of LargitData, a startup company that mainly focuses on providing big data and machine learning products.

Do you find GIS & Spatial Data books & manuals too vague, expensive & not practical and looking for a course that takes you by hand, teaches you all the concepts, and get you started on a real life project? Or perhaps you want to save time and learn how to automate some of the most common GIS tasks? I'm very excited you found my spatial data analysis course. My course provides a foundation to carry out PRACTICAL, real-life spatial data analysis tasks in popular and FREE software frameworks. My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate.

This course is designed to take users who use R and QGIS for basic spatial data/GIS analysis to perform more advanced GIS tasks (including automated workflows and geo-referencing) using a variety of different data. In addition to making you proficient in R and QGIS for spatial data analysis, you will be introduced to another powerful free GIS software.. GRASS. This course takes a completely practical approach to spatial data analysis and mapping- Each lecture will teach you a practical application/processing technique which you can apply easily. The course is taught by Minerva Singh, A PhD graduate from Cambridge University, UK, who has several years of research experience in Quantitative Ecology and an MPhil in Geography and Environment from Oxford University. Minerva has published papers in international peer reviewed journals and given talks at international conferences.