Solve problems using data science, machine learning practically and build real world projects using python
What you will learn
Learn best practices for real-world data sets.
Create supervised machine learning algorithms to predict classes.
Create regression machine learning algorithms for predicting continuous values.
Learn to use Pandas for Data Analysis.
Description
Machine learning is one modern innovation that has helped man enhance not only many industrial and professional processes but also advances everyday living. But what is machine learning? It is a subset of artificial intelligence, which focuses on using statistical techniques to build intelligent computer systems in order to learn from databases available to it. Currently, machine learning has been used in multiple fields and industries. For example, medical diagnosis, image processing, prediction, classification, learning association, regression, etc.
Classification is a process of placing each individual understudy in many classes. Classification helps to analyze the measurements of an object to identify the category to which that object belongs. To establish an efficient relation, analysts use data. For example, before a bank decides to distribute loans, it assesses the customers on their ability to pay loans. By considering the factors like customers’ earnings, savings, and financial history, we can do it. This information is taken from the past data on the loan.
Machine learning can also be used in prediction systems. Considering the loan example, to compute the probability of a fault, the system will need to classify the available data in groups. It is defined by a set of rules prescribed by the analysts. Once the classification is done, we can calculate the probability of the fault. These computations can compute across all the sectors for varied purposes. Making predictions is one of the best machine learning applications.
The extraction of information is one of the best applications of machine learning. It is the process of extracting structured information from unstructured data. For example, web pages, articles, blogs, business reports, and emails. The relational database maintains the output produced by the information extraction. The process of extraction takes a set of documents as input and outputs the structured data.
We can also implement machine learning in the regression as well. In regression, we can use the principle of machine learning to optimize the parameters. It can also be used to decrease the approximation error and calculate the closest possible outcome. We can also use machine learning for function optimization. We can also choose to alter the inputs in order to get the closest possible outcome.