Machine Learning with Python培训

课程编码

mlpython

课程时长

21 小时 通常来说是3天,包括中间休息。

要求

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

课程概览

本课程的目的是提供在实践中应用机器学习方法的一般熟练程度。通过使用 Python 编程语言及其各种库, 并基于大量的实际示例, 本课程教授如何使用机器学习最重要的构建块, 如何做出数据建模决策, 解释输出并验证结果

我们的目标是让您能够自信地理解和使用机器学习工具箱中最基本的工具, 并避免数据科学应用的常见陷阱。

Machine Translated

课程大纲

Introduction to Applied Machine Learning

  • Statistical learning vs. Machine learning
  • Iteration and evaluation
  • Bias-Variance trade-off

Supervised Learning and Unsupervised Learning

  • Machine Learning Languages, Types, and Examples
  • Supervised vs Unsupervised Learning

Supervised Learning

  • Decision Trees
  • Random Forests
  • Model Evaluation

Machine Learning with Python

  • Choice of libraries
  • Add-on tools

Regression

  • Linear regression
  • Generalizations and Nonlinearity
  • Exercises

Classification

  • Bayesian refresher
  • Naive Bayes
  • Logistic regression
  • K-Nearest neighbors
  • Exercises

Cross-validation and Resampling

  • Cross-validation approaches
  • Bootstrap
  • Exercises

Unsupervised Learning

  • K-means clustering
  • Examples
  • Challenges of unsupervised learning and beyond K-means

Neural networks

  • Layers and nodes
  • Python neural network libraries
  • Working with scikit-learn
  • Working with PyBrain
  • Deep Learning

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