This is nocoder101's ML 101 repo. It includes some book examples and sample codes about ML that I've run. Related book list are below; And I'm also learning Hadoop and relevant platforms&technologies like Apache Spark, Kafka to implement

• Hands-on Machine Learning with Scikit-Learn & TensorFlow by Aurelien Geron (O'reilly Media Inc., 2017)
  • https://github.com/rickiepark/handson-ml/
• Data Science Handbook by Field Cady (John Wiley&Sons.Inc., 2017)
• Real-world Machine Learning by Henrik Brink, Joseph W.Rechards and Mark Feiheroff(Manning, 2016)
• (reserved)

• Advanced Analytics with Spark by Sanford Ryza, Uri Laserson, Sean Owen, and Joshua Wills(O'relly Media Inc., 2017, 2nd edition)

• Scikit-learn(ML in Python)

  • Intro

    • http://scikit-learn.org/
    • Classification, Regression, Clustering, Dimensionality Reduction, Model Selection, Preprocessing
    • BSD license: Open source, Commercially usable

  • Categories

    • Classification: identifying to which category an object belongs to
      • http://scikit-learn.org/stable/supervised_learning.html#supervised-learning
      • (spam detection, image recognition)
      • SVM(Support Vector Machine)
      • nearest neighbors
      • random forest
    • Regression: prdeicting a continulus-valued attribute associated with an object
      • http://scikit-learn.org/stable/supervised_learning.html#supervised-learning
      • (drug response, stock prices)
      • SVR(Support Vector Regression)
      • ridge regression
      • lasso
    • Clustering: automatic grouping of similar objects into sets
      • http://scikit-learn.org/stable/modules/clustering.html#clustering     - (customer segmentation, group experiment outcomes)     - k-means
      • spectral clustering
      • mean-shift
    • Dimensionality Reduction: recuding the number random variables to consider
      • http://scikit-learn.org/stable/modules/decomposition.html#decompositions     - ㄴ visualization, increased efficiency     - PCA
      • reature selection
      • non-negative matrix factorization
    • Model Selection: comparing, validating and choosing parameters and models
      • http://scikit-learn.org/stable/model_selection.html#model-selection     - ㄴ improved accuracy via parameter tuning
      • grid search
      • cross validation
      • metrics
    • Preprocessing: feature extraction and normalization     - ㄴ tranforming input data such as text for use with ML algorithms
      • preprocessing
      • feature extraction

  • Regression

• TensorFlow
  • https://www.tensorflow.org/
  • An open source machine learning framework for everyone