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  • Python for Data Science

    Python for Machine Learning

    Python for Data Visualization

    Libraries
    Matplotlib
    Seaborn
    ggplot
    GraphX
    Plotly
    Functions
    Exploratory data analysis
    Data storytelling
    Decision-support dashboard design
    Public education(news, media, and data blogging)

    Python for Machine Learning

    Libraries
    scikit-learn
    Tensor Flow
    PyTorch
    Functions (By Use Case)
    Regression
    Clustering
    Dimension reduction
    Association rules
    Deep learning
    Instance-based
    Decision trees
    Bayesian
    Ensemble
    Regularization

    Python for Data Engineering

    Functions

    • Learn to build simple MapReduce jobs (sans Java)
    • Write Spark jobs(sans Scala)
    • Programming IoT device(Raspberry Pi)
    • Building ETL processes(Airflow)

    Types of Machine Learning Methods

    Supervised Learning Making predictions straight from labeled data
    Unsupervised Learning Making predictions straight from unlabeled data
    Semi-Supervised Learning Uses both labeled and unlabeled data to make a set of predictions

    Popular Ways to Group ML Algorithms

    By Learning By Function By Use Case
    Supervised Regression Fraud detection
    Unsupervised Clustering Recommendation engines
    Semi-supervised Dimension reduction Price forecasting
    Association rules Inventory demand forecasting
    Deep learning Water consumption forecasting
    Instance-based Infrastructure demand forecasting
    Decision trees And so on
    Bayesian
    Ensemble
    Regularization
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  • 原文地址:https://www.cnblogs.com/keepmoving1113/p/14317703.html
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