Face Recognition is a recognition technique used to detect faces of individuals whose images saved in the data set. Despite the point that other methods of identification can be more accurate, face recognition has always remained a significant focus of research because of its non-meddling nature and because it is people's facile method of personal identification. Face recognition algorithms classified as geometry based or template based algorithms. The template-based methods can be constructed using statistical tools like SVM [Support Vector Machines], PCA [Principal Component Analysis], LDA [Linear Discriminant Analysis], Kernel methods or Trace Transforms. The geometric feature based methods analyse local facial features and their geometric relationship.
About this course: Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more.
Apache Spark lets you apply machine learning techniques to data in real time, giving users immediate machine-learning based insights based on what's happening right now. It's used to create machine learning models and programs that are distributed and much faster compared to standard machine learning toolkits such as R or Python. If you're a data professional who is familiar with machine learning and wants to use Apache Spark for developing efficient and fast machine learning systems, then this learning path is for you. This comprehensive 2-in-1 course teaches you to build machine learning systems, perform analytics, and predictions with Apache Spark. You'll learn through practical demonstrations of use cases, clear explanations, and interesting real-world applications.
Check out CryptoDataDownload's Statistical Analysis section for updates as we build this out! These measures rely on math and are used for both risk management and finding market inefficiencies or opportunities. There are many resources available, but CryptoDataDownload recommends you visit PythonProgramming.net
When techniques like linear regression or time series were aimed at modelling the general trend exhibited by a set or series of data points, data scientists faced another question - though these models can capture the overall trend but how can one model the volatility in the data? In real life, the initial stages in a business or a new market are always volatile and changing with a high velocity until things calm down and become saturated. It is then one can apply the statistical techniques such as time series analysis or regression as the case may be. To go into the turbulent seas of volatile data and analyze it in a time changing setting, ARCH models were developed. As I already mentioned, ARCH is a statistical model for time series data.
Recently, during my coursework, I have been left in awe of the advancements in the field of science in general. In just about a decade, we have completely revolutionized the way we look at the capabilities of machines, the way we build software and much more. Tasks that seemed impossible just a decade ago have become accessible and effortless. Long story short, we have made the machines think!! Sounds cool, isn't it? Artificial Intelligence has truly entered the mainstream consciousness.
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist's toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated.