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Machine Learning Projects with Python

Learn Machine Learning by Building real life AI projects

Machine Learning Projects with Python

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Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.


In this course you will build 3 basic real life machine learning projects:


Project #1: House Price Prediction using Machine Learning

In this project we will build a artificial intelligence model that predicts house prices using sklearn multiple linear regression algortihm.


Project #2: Salary Calculation using Machine Learning

It is a tedious work to calculate each employee’s salary according to employee’s experience level. In this project we are going to build a machine learning model for exact calculation of employee salaries. Since most of salary values are non-linear, a simple linear function can not be used for this calculation process. Generally most of the companies have polynomial salary values for their employees. Therefore we will use polynomial linear regression algorithm for solution here.


Project #3: Advanced Customer Segmentation using Machine Learning

In this project, we will use a new and advanced segmentation library developed by the Massachusetts Institute of Technology (MIT). The customer data in our Customer Segmentation project, which is included in the entry and intermediate level projects, was simple and the K-Means clustering algorithm was sufficient for segmentation. But life is not that simple! When you have complex customer data, if you do clustering with K-Means, you may get erroneous results! Since the customer data in this project is complex data (both numeric and categorical) just like in real life, here we will use a special unsupervised learning algorithm instead of a standard model and divide our 2000 customers into groups with the latest artificial intelligence algorithms.

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