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    Unsupervised Machine Learning with Python

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    Unsupervised Machine Learning with Python - Online Course Download
    Unsupervised Machine Learning with Python - Online Course Download
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    free online course free udemy paid course freecourse freecoursesite IT & Software Other IT & Software udemy course download udemy free download Unsupervised Machine Learning

    What you'll learn :

    Clustering Algorithms: Hierarchical, DBSCAN, K Means, Gaussian Mixture Model
    Dimensions Reduction: Principal Component Analysis (PCA)
    Implementation of clustering algorithms and principal component analysis in Python
    Applications of clustering and PCA using real-world data

    Requirements :

    Basic knowledge of Linear Algebra including vectors, matrices, transpose, matrix multiplications, linear spaces
    Basic knowledge of Probability and Statistics including mean, covariance, and normal distributions
    Ability to program in Python 3
    Ability to run Python 3 programs on the local machine in Jupyter notebooks and command window

    Description :

    Unsupervised Machine Learning involves finding patterns in datasets.

    After taking this course, students will be able to understand and implement in Python algorithms of Unsupervised Machine Learning to real-world datasets.

    This course is designed for:

    Scientists, engineers, and programmers and others interested in machine learning/data science

    No prior experience with machine learning is needed

    Students should have knowledge of

    Basic linear algebra (vectors, transpose, matrices, matrix multiplication, inverses, determinants, linear spaces)

    Basic probability and statistics (mean, covariance matrices, normal distributions)

    Python 3 programming

    The core of this course involves detailed study of the following algorithms:

    Clustering: Hierarchical, DBSCAN, K Means & Gaussian Mixture Model

    Dimension Reduction: Principal Component Analysis

    The course presents the math underlying these algorithms including normal distributions, expectation maximization, and singular value decomposition. The course also presents detailed explanation of code design and implementation in Python, including use of vectorization for speed up, and metrics for measuring quality of clustering and dimension reduction.

    The course codes are then used to address case studies involving real-world data to perform dimension reduction/clustering for the Iris Flowers Dataset, MNIST Digits Dataset (images), and BBC Text Dataset (articles).

    Plenty of examples are presented and plots and animations are used to help students get a better understanding of the algorithms.

    Course also includes a number of exercises (theoretical, Jupyter Notebook, and programming) for students to gain additional practice.

    All resources (presentations, supplementary documents, demos, codes, solutions to exercises) are downloadable from the course Github site.

    Students should have a Python installation, such as the Anaconda platform, on their machine with the ability to run programs in the command window and in Jupyter Notebooks

    Who this course is for :

    Scientists, engineers and programmers interested in data science/machine learning

    Course Size Details :

    9.5 hours on-demand video
    29 downloadable resources
    Full lifetime access
    Access on mobile and TV
    Certificate of completion

    View Demo

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