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Machine Learning

FREE (Audit)
108 hours
English
Columbia
Course by
edXCourses from edX
Certificate awarded
For Rs. 26583
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Checklist

Certification

You will get a certificate on completing this course.

University

The course is from one of the top universities of the world - Columbia.

Price

This course is costly - Rs. 26583/-.

Difficulty

The students of this course have found this course difficult.

Content

The students of this course have liked the content of this course.

Assignments

The students of this course have not liked the assignments of this course.

Teaching

The students of this course have liked how the instructor has taught this course.

Satisfaction

The students of this course are overall satisfied with this course.

Edvicer's Rewards

You can get a cashback of ₹ 1000 on buying this course.

Why should you choose this course?

Description

Machine Learning is the basis for the most exciting careers in data analysis today. You’ll learn the models and methods and apply them to real world situations ranging from identifying trending news topics, to building recommendation engines, ranking sports teams and plotting the path of movie zombies. Major perspectives covered include: probabilistic versus non-probabilistic modeling supervised versus unsupervised learning Topics include: classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection. Methods include: linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others. In the first half of the course we will cover supervised learning techniques for regression and classification. In this framework, we possess an output or response that we wish to predict based on a set of inputs. We will discuss several fundamental methods for performing this task and algorithms for their optimization. Our approach will be more practically motivated, meaning we will fully develop a mathematical understanding of the respective algorithms, but we will only briefly touch on abstract learning theory. In the second half of the course we shift to unsupervised learning techniques. In these problems the end goal less clear-cut than predicting an output based on a corresponding input. We will cover three fundamental problems of unsupervised learning: data clustering, matrix factorization, and sequential models for order-dependent data. Some applications of these models include object recommendation and topic modeling.

Syllabus

Supervised learning techniques for regression and classification
Unsupervised learning techniques for data modeling and analysis
Probabilistic versus non-probabilistic viewpoints
Optimization and inference algorithms for model learning

What others say about this course

Reviews from Class Central

"> Buckle up -- this deep, mathematically rigorous dive into the major areas of machine learning is fast-paced and challenging. In fact, most of the course is less about machine learning than the m  Read More ...

"> This is the most insightful course I cam across. Although I am a trained practitioner of ML concepts, but there were some topics like Gaussian Processes, Collaborative Filtering, LDA etc, for whi  Read More ...

"> The materials are mathematically rigorous and really provide insight on how to analyse, design and evaluate learning algorithms. Prof Paisley lectures are dense, though unfortunately he's not the  Read More ...

"> I am brand new in this field and would like to explore how it can be used in my work. I am also new in python programming but experienced in programming in VBA. With this two facts, I still found  Read More ...

"> The course provides a solid theoretical introduction in Machine Learning. The concepts are efficiently presented through the video lectures by Prof. Paisley and there are 4 programming assignment  Read More ...

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