shajidHossainHemal/Coursera--Machine-Learning-with-Python-by-IBM shajidHossainHemal/Coursera--Machine-Learning-with-Python-by-IBM

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shajidHossainHemal/Coursera--Machine-Learning-with-Python-by-IBM
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About this Course This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.

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Collaborative-Filtering-Recommendation-Engine

Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore recommendation systems based on Collaborative Filtering and implement simple version of one using Python and the Pandas library. Collaborative filtering is based on the fact that relationships exist between products and people's interests. Many recommendation systems use collaborative filtering to find these relationships and to give an accurate recommendation of a product that the user might like or be interested in. Collaborative filtering has basically two approaches: user-based and item-based. User-based collaborative filtering is based on the user similarity or neighborhood. Item-based collaborative filtering is based on similarity among items. Let's first look at the intuition behind the user-based approach. In user-based collaborative filtering, we have an active user for whom the recommendation is aimed. The collaborative filtering engine first looks for users who are similar. That is users who share the active users rating patterns. Collaborative filtering basis this similarity on things like history, preference, and choices that users make when buying, watching, or enjoying something.
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