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C1 The Machine Learning Landscap

C1 The Machine Learning Landscap

作者: 闫_锋 | 来源:发表于2018-07-18 10:39 被阅读11次

    Application: OCR, spam filter

    To summarize, Machine Learning is great for:
    • Problems for which existing solutions require a lot of hand-tuning or long lists of
    rules: one Machine Learning algorithm can often simplify code and perform bet‐
    ter.
    • Complex problems for which there is no good solution at all using a traditional
    approach: the best Machine Learning techniques can find a solution.
    • Fluctuating environments: a Machine Learning system can adapt to new data.
    • Getting insights about complex problems and large amounts of data.
    

    Supervised:
    KNN
    Linear Regression
    Logistic Regression
    SVM
    Decision Trees and Random Forests
    Neural Networks

    Unsupervised:
    Clustering:
    k-means
    Hierarchical Cluster Analysis
    Expectation Maximization
    Visualization and dimensionality reduction
    Principal Component Analysis
    Kernel PCA
    Locally-Linear Embedding
    t-distributed Stochastic Neighbor Embedding (t-SNE)
    Association rule learning
    Apriori
    Eclat

    Feature Extraction
    Anomaly detection

    Deep belief networks
    Restricted Boltzmann machines

    Batch vs Online Learning (mini-batch)

    Main Challenges of Machine Learning
    Insufficient Quantity of Training Data
    Nonrepresentative Training Data
    Poor-Quality Data
    Irrelevant Features

    Overfitting the Training Data
    (Overfitting happens when the model is too complex relative to the
    amount and noisiness of the training data. ) -> degrees of freedom

    Lastly, your model needs to be neither too simple (in which case it will
    underfit) nor too complex (in which case it will overfit).

    cross-validation

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