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Business Problems and Data Science Solutions Part 1

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An important principle of data science is that data mining is a process. It includes the application of information technology, such as the automated discovery and evaluation of patterns from data. It also includes an analyst's creativity, business knowledge, and common sense. Understanding the whole process helps to structure data mining projects. Since the data mining process breaks up the overall task of finding patterns from data into a set of well-defined subtasks, it is also useful for structuring discussions about data science.


Supervised vs. Unsupervised Learning

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Within the field of machine learning, there are two main types of tasks: supervised, and unsupervised. The main difference between the two types is that supervised learning is done using a ground truth, or in other words, we have prior knowledge of what the output values for our samples should be. Therefore, the goal of supervised learning is to learn a function that, given a sample of data and desired outputs, best approximates the relationship between input and output observable in the data. Unsupervised learning, on the other hand, does not have labeled outputs, so its goal is to infer the natural structure present within a set of data points. Supervised learning is typically done in the context of classification, when we want to map input to output labels, or regression, when we want to map input to a continuous output.


MACHINE LEARNING FOR BEGINNERS

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Hi everyone, I'm reposting all of my old blogs, as my account got hacked. This blog was originally published on March 6, 2019. Within the field of machine learning, there are two main types of tasks: supervised, and unsupervised. The main difference between the two types is that supervised learning is done using a ground truth, or in other words, we have prior knowledge of what the output values for our samples should be. Therefore, the goal of supervised learning is to learn a function that, given a sample of data and desired outputs, best approximates the relationship between input and output observable in the data.



The Machine Learning Algorithms Used in Self-Driving Cars

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Today, the machine learning algorithms are extensively used to find the solutions to various challenges arising in manufacturing self-driving cars. With the incorporation of sensor data processing in an ECU (Electronic Control Unit) in a car, it is essential to enhance the utilization of machine learning to accomplish new tasks. The potential applications include evaluation of driver condition or driving scenario classification through data fusion from different external and internal sensors – like lidar, radars, cameras or the IoT (Internet of Things). The applications that run the infotainment system of a car can receive the information from sensor data fusion systems and for example, have the capability to direct the car to a hospital if it notices that something is not right with the driver. This application based on machine learning also includes the driver's speech and gesture recognition and language translation.