Course Outline

Pengantar ke Applied Machine Learning

  • Pembelajaran statistik vs. Pembelajaran mesin
  • Iterasi dan evaluasi
  • Trade-off Bias-Variance

Supervised Learning dan Unsupervised Learning

  • Machine Learning Languages, Jenis, dan Contoh
  • Supervised vs Unsupervised Learning

Supervised Learning

  • Decision Trees
  • Random Forests
  • Evaluasi Model

Machine Learning dengan Python

  • Pilihan libraries
  • Add-on tools

Regresi

  • Regresi linear
  • Generalisasi dan Nonlinearitas
  • Latihan

Klasifikasi

  • Penyegaran Bayesian
  • Naive Bayes
  • Regresi logistik
  • K-Nearest neighbors
  • Latihan

Cross-validation dan Resampling

  • Pendekatan cross-validation
  • Bootstrap
  • Latihan

Unsupervised Learning

  • K-means clustering
  • Contoh
  • Tantangan pembelajaran tanpa pengawasan dan melampaui K-means

Neural networks

  • Layers dan nodes
  • Python neural network libraries
  • Bekerja dengan scikit-learn
  • Bekerja dengan PyBrain
  • Deep Learning

Requirements

Knowledge of Python programming language. Basic familiarity with statistics and linear algebra is recommended.

 28 Hours

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