The course will mostly focus on helping you implement different statistical analysis techniques on your data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects immediately! TAKE ACTION NOW:) You'll also have my continuous support when you take this course just to make sure you're successful with it. If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case you're not completely satisfied with the course.

OpenCV is a library of programming functions mainly aimed at real-time computer vision. This course will show you how machine learning is great choice to solve real-word computer vision problems and how you can use the OpenCV modules to implement the popular machine learning concepts. The video will teach you how to work with the various OpenCV modules for statistical modelling and machine learning. You will start by preparing your data for analysis, learn about supervised and unsupervised learning, and see how to implement them with the help of real-world examples. The course will also show you how you can implement efficient models using the popular machine learning techniques such as classification, regression, decision trees, K-nearest neighbors, boosting, and neural networks with the aid of C and OpenCV.

If you are looking to build data science models that are good for production, Java has come to the rescue. This unique video provides modern solutions to solve your common and not-so-common data science-related problems. We start with solutions to help you obtain, clean, index and search data. Then you will learn a variety of techniques to analyze data. By the end of this course, you will be able to perform all advanced operations it takes to analyze the complexity of data and to perform indexing and search operations.

"Human plus machine isn't the future, it's the present," Garry Kasparov said in a recent TED talk. And this "present" is transforming the world of education at a rapid pace. With children increasingly using tablets and coding becoming part of national curricula around the world, technology is becoming an integral part of classrooms, just like chalk and blackboards. We have already witnessed the rise and impact of education technology especially through multitude of adaptive learning platforms such as Khan Academy and Coursera that allow learners to strengthen their skills and knowledge. And now virtual reality (VR) and artificial intelligence (AI) are gaining traction. A recent report by Pearson deciphers how artificial intelligence will positively transform education in the coming years.

The main topics concerning mathematics that you should familiarize yourself with if you want to go into data science are probability, statistics, and linear algebra. As you learn more about other topics such as statistical learning (machine learning) these core mathematical foundations will serve as a base for you to continue learning from. Let's briefly describe each and give you a few resources to learn from! Probability -- is the measure of the likelihood that an event will occur. A lot of data science is based on attempting to measure likelihood of events, everything from the odds of an advertisement getting clicked on, to the probability of failure for a part on an assembly line. For this classic topic I recommend going with a book, such as A First Course in Probability by Sheldon Ross or Probability Theory by E.T. Jaynes.

"Human plus machine isn't the future, it's the present," Garry Kasparov said in a recent TED talk. And this "present" is transforming the world of education at a rapid pace. With children increasingly using tablets and coding becoming part of national curricula around the world, technology is becoming an integral part of classrooms, just like chalk and blackboards. We have already witnessed the rise and impact of education technology especially through multitude of adaptive learning platforms such as Khan Academy and Coursera that allow learners to strengthen their skills and knowledge. And now virtual reality (VR) and artificial intelligence (AI) are gaining traction. A recent report by Pearson deciphers how artificial intelligence will positively transform education in the coming years.

The real power and value proposition of Apache Spark is its speed and platform to execute Data Science tasks. Spark's unique use case is that it combines ETL, batch analytic, real-time stream analysis, machine learning, graph processing, and visualizations to allow Data Scientists to tackle the complexities that come with raw unstructured data sets. Spark embraces this approach and has the vision to make the transition from working on a single machine to working on a cluster, something that makes data science tasks a lot more agile. In this course, you'll get a hands-on technical resource that will enable you to become comfortable and confident working with Spark for Data Science. We won't just explore Spark's Data Science libraries, we'll dive deeper and expand on the topics.

R is one of the most popular languages used for machine learning and arguably, the best entry point to the fascinating world of machine learning (ML). If you're interested to explore both the programming and machine learning world with R, then go for this course. This course is a blend of text, videos, code examples, assessments, case studies, and a mini project which together makes your learning journey all the more exciting and truly rewarding. It is meticulously designed and developed in order to empower you with all the right and relevant information on R. Let's take a look at this learning journey. The course starts with teaching you how to set up the R environment, which includes installing RStudio and R packages. You will learn the various data types, operators, and control structures.

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Java is arguably the single most important technology out there. Core Java Programming is an excellent introduction in to the world of Java programming. The instructor will take you through the basics of Java syntax and the complexities of Object Oriented Programming. This course is a stand-alone course, however it would be a huge aid to the online student who is taking a self-directed course, an individual who is trying to learn how to program. At the end of this course, you will be well versed with how to program in Java from the very basic level to an intermediate level of programming.