5/22/2019
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Basic Concepts Of Machine ( 9 REVIEWS )
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This course focus on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning. It …
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1 YEAR LEVEL 2 - CERTIFICATE COURSE BADGE COURSE CERTIFICATE
25
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4 HOURS, 20 MINUTES
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This course focus on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning. It covers topics such as classi cation and linear regression and ending up with more recent topics like boosting hidden Markov models, and Bayesian networks. It will give the student the basic ideas as well as a bit more formal understanding of how, why, and when they work.
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.
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Course Credit: MIT
COURSE CURRICULUM
Module 01
Introduction, linear classi cation, perceptron update rule
00:10:00
Perceptron convergence, generalization
00:15:00
Maximum margin classi cation
00:10:00
Classi cation errors, regularization, logistic regression
00:10:00
Linear regression, estimator bias and variance, active learning
00:15:00
Active learning (cont.), non-linear predictions, kernals
00:10:00
Kernal regression, kernels
00:10:00
Support vector machine (SVM) and kernels, kernel optimization
00:15:00
Model selection
00:15:00
Model selection criteria
00:10:00
Description length, feature selection
00:10:00
Combining classi ers, boosting
00:10:00
Module 02
Boosting, margin, and complexity
00:10:00
Margin and generalization, mixture models
00:10:00
https://edukite.org/course/basic-concepts-machine/
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Basic Concepts Of Machine - Edukite
Mixtures and the expectation maximization (EM) algorithm
00:15:00
EM, regularization, clustering
00:10:00
Clustering
00:10:00
Spectral clustering, Markov models
00:10:00
Hidden Markov models (HMMs)
00:15:00
HMMs (cont.)
00:10:00
Bayesian networks
00:10:00
Learning Bayesian networks
00:10:00
Probabilistic inference
00:10:00
Assessment
Submit Your Assignment
00:00:00
Certi cation
00:00:00
COURSE REVIEWS
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