Time Series Analysis can feel familiar and completely foreign at the same time, even to experienced data scientists. It plays by a similar, yet different, set of rules compared to typical classification or regression modeling. Still, Time Series Analysis has applications across industries. Familiar applications such as demand prediction to properly stock the shelves of a store or generate enough electricity to power a city, and less familiar applications such as signal classification to detect level shifts or changes in the underlying behavior of a time series to detect market shifts early. Delving into the world of Time Series Analysis is significantly easier in a low-code environment, enabling the learning and application of new techniques without the requirement of learning new coding libraries at the same time.

While the debate regarding how much screen time is appropriate for children rages on among educators, psychologists, and parents, it's another emerging technology in the form of artificial intelligence and machine learning that is beginning to alter education tools and institutions and changing what the future might look like in education. It is expected that artificial intelligence in U.S. education will grow by 47.5% from 2017-2021 according to the Artificial Intelligence Market in the US Education Sector report. Even though most experts believe the critical presence of teachers is irreplaceable, there will be many changes to a teacher's job and to educational best practices. AI has already been applied to education primarily in some tools that help develop skills and testing systems. As AI educational solutions continue to mature, the hope is that AI can help fill needs gaps in learning and teaching and allow schools and teachers to do more than ever before.

With the evolution of technology, consumer behavior also continues to evolve. Naturally, to stay ahead of the competitive curve the retailers need to make more rigorous use of the customer data. As the data volume is increasing at a rapid pace Big Data analytics are being utilized by retailers to use the most relevant customer insights. But over time, even gathering large volumes of multifaceted user data for analytics didn't prove to be much useful. This is where new technologies like Artificial Intelligence (AI) and Machine Learning (ML) cane with bigger promises.

While the debate regarding how much screen time is appropriate for children rages on among educators, psychologists, and parents, it's another emerging technology in the form of artificial intelligence and machine learning that is beginning to alter education tools and institutions and changing what the future might look like in education. It is expected that artificial intelligence in U.S. education will grow by 47.5% from 2017-2021 according to the Artificial Intelligence Market in the US Education Sector report. Even though most experts believe the critical presence of teachers is irreplaceable, there will be many changes to a teacher's job and to educational best practices. AI has already been applied to education primarily in some tools that help develop skills and testing systems. As AI educational solutions continue to mature, the hope is that AI can help fill needs gaps in learning and teaching and allow schools and teachers to do more than ever before.

As we know from the basic maths that if we plot an'X','Y' graph, a linear relationship will always come up with a straight line. The equation of a straight line is written using the y mx b, where m is the slope (Gradient) and b is y-intercept (where the line crosses the Y axis). Once we get the equation of a straight line from 2 points in space in y mx b format, we can use the same equation to predict the points at different values of x which result in a straight line. In this formula, m is the slope and b is y-intercept. Let's take a real world example to demonstrate the usage of linear regression and usage of Least Square Method to reduce the errors Let's take a real world example of the price of agricultural products and how it varies based on the location its sold.

At a time and age which scientists and biologists are calling the Human Age, it might actually be more difficult to find a problem where machine learning hasn't already been applied to. We could very well be quickly entering the next epoch; the Machine Age. Machine learning is everywhere, from finance to science to social... Machine Learning Example #1 - Finance There is an increasing amount of machine learning to be found in finance. A trend that goes hand in hand with increased computer power and the availability of machine learning tools (e.g Google's Tensorflow). Which brings us to the relatively newly formed term of'robo-adviser': A financial portfolio managed by a machine learned robot.

At a time and age which scientists and biologists are calling the Human Age, it might actually be more difficult to find a problem where machine learning hasn't already been applied to. We could very well be quickly entering the next epoch; the Machine Age. Machine learning is everywhere, from finance to science to social... Machine Learning Example #1 - Finance There is an increasing amount of machine learning to be found in finance. A trend that goes hand in hand with increased computer power and the availability of machine learning tools (e.g Google's Tensorflow). Which brings us to the relatively newly formed term of'robo-adviser': A financial portfolio managed by a machine learned robot.

Azure Machine Learning is Microsoft's machine learning studio. It provides a workbench for analysts to perform data analysis including applying predictive analytics and machine learning algorithms. One of the key uses of Machine Learning is finding correlations in data and using the relationships between different indicators to provide predictive power. Here is an example scenario I built in Azure ML. I found a dataset that describes a set of Community Health Status Indicators by county for the United States.

Using some real world examples I illustrate the important role of multiobjective optimization in decision making and its interface with preference handling. I explain what optimization in the presence of multiple objectives means and discuss some of the most common methods of solving multiobjective optimization problems using transformations to single objective optimisation problems. Finally, I address linear and combinatorial optimization problems with multiple objectives and summarize techniques for solving them. Throughout the article, I refer to the real world examples introduced at the beginning.