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Prediction is the heart of scientific discipline, and generalization from data is the basis of machine learning and statistics. Fields aim at the field are, the prediction from data. It provides a selection of the important topics from both of these subjects. Machine learning and statistical methods are used the scientific world for their use in handling the “information overload” that characterizes our current digital age.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer need to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

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Course Credit: MIT

Course Curriculum

Rule mining and the Apriori algorithm 00:15:00
R for machine learning 00:16:00
Fundamentals of learning 00:15:00
Inference 00:14:00
Clustering 00:13:00
k-nearest neighbors 00:15:00
Naïve Bayes 00:14:00
Decision trees 00:14:00
Logistic regression 00:14:00
Boosting 00:13:00
Convex optimization 00:13:00
Support vector machines 00:16:00
Kernels 00:15:00
Statistical learning theory 00:14:00
Bayesian analysis 00:12:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

Course Reviews

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