A Comparison of 2 Top Machine Learning MOOCs

Posted in 'Course Spotlights' started by MoocLab, Jul 23, 2018.

  1. MoocLab

    MoocLab Official Account Group Manager

    Machine learning is a form of artificial intelligence (AI) that teaches computers to learn in the same way humans do by interpreting data and using it to learn and improve themselves without human intervention. Examples of machine learning can be found in many situations, from understanding and predicting how a disease evolves to matching products to online shoppers based on their behaviour.

    The scope and possibilities for Machine Learning today are huge and businesses are now going all out to recruit competent engineers to help improve their performance. According to salary information website PayScale, a Machine Learning Engineer earns an average salary of $100,956 per year.

    Below, we look at two of the best MOOCs in Machine learning offered on EdX and Coursera.

    At a Glance
    1EdXColumbia UniversityAdvanced12 weeks8-10 hrs/courseFree$199
    2CourseraStanford UniversityIntermediate11 weeks5-7 hrs/courseFree£59
    *The length and effort are recommendations given by the course provider

    1. Machine Learning by Columbia University via EdX
    About this course
    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.

    What you'll learn
    • 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
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    2. Machine Learning by Stanford University via Coursera
    About this course
    Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

    This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

    Go to Course ...


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