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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-with-python-a-mathematical-perspective/
课程评论：没有评论
课程名称：使用Python的机器学习：数学视角 课程概述： 本课程通过数学的视角深入探讨机器学习的基本原理和应用。首先介绍机器学习的三种基本类型，以及相关的基本术语和符号，提供构建机器学习系统的路线图。学员将学习如何使用Python进行机器学习，包括： - 训练简单的分类机器学习算法，回顾人工神经元的早期历史。 - 在Python中实现感知器学习算法，以及适应性线性神经元的学习收敛性。 - 通过使用scikit-learn进行机器学习分类算法的探索，包括选择分类算法、训练感知器和使用逻辑回归建模类概率。 - 最大间隔分类（支持向量机）、非线性问题的解决（核SVM）、决策树学习和K近邻（懒惰学习算法）。 数据预处理和超参数调整： 课程还涵盖数据预处理的方法，包括处理缺失数据、分类数据的处理、数据集的划分、特征归一化以及特征选择的重要性评估。介绍随机森林特征重要性评估、降维技术（主成分分析和线性判别分析），以及最佳实践的模型评估和超参数调整。 回归分析： 学员将学习如何预测连续目标变量，涵盖线性回归的基本概念、数据集的探索、普通最小二乘回归模型的实现、鲁棒回归模型的拟合等内容。还将研究使用正则化方法的回归技巧和多项式回归来处理非线性关系。 无监督数据分析与聚类： 介绍通过相似性对对象进行分组的k均值聚类、层次聚类以及使用DBSCAN定位高密度区域的技巧。 多层人工神经网络与深度学习： 学习使用人工神经网络建模复杂函数、数字分类、网络训练及其收敛性的话题。此外，还将涉及使用TensorFlow的神经网络训练的并行化及性能优化。 通过全面的学习，参与者将掌握机器学习的核心算法、数据处理技术和深度学习的基本框架，能够有效应用于实际问题解决中。
Machine Learning: The three different types of machine learning, Introduction to the basic terminology and notations, A roadmap for building machine learning systems, Using Python for machine learning Training Simple Machine Learning Algorithms for Classification, Artificial neurons - a brief glimpse into the early history of machine learning, Implementing a perception learning algorithm in Python, Adaptive linear neurons and the convergence of learning A Tour of Machine Learning Classifiers Using scikit-learn, Choosing a classification algorithm, First steps with scikit-learn - training a perceptron, Modeling class probabilities via logistic regression, Maximum margin classification with support vector machines, Solving nonlinear problems using a kernel SVM, Decision tree learning, K-nearest neighbors - a lazy learning algorithm. Data Preprocessing, Hyperparameter Tuning: Building Good Training Sets, Dealing with missing data, Handling categorical data, Partitioning a dataset into separate training and test sets, Bringing features onto the same scale, Selecting meaningful features, Assessing feature importance with random forests, Compressing Data via Dimensionality Reduction, Unsupervised dimensionality reduction via principal component analysis, Supervised data compression via linear discriminant analysis, Using kernel principal component analysis for nonlinear mappings, Learning Best Practices for Model Evaluation and Hyperparameter Tuning, Streamlining workflows with pipelines, Using k-fold cross-validation to assess model performance. Regression Analysis: Predicting Continuous Target Variables, Introducing linear regression, Exploring the Housing dataset, Implementing an ordinary least squares linear regression model, Fitting a robust regression model using RANSAC, Evaluating the performance of linear regression models, Using regularized methods for regression, Turning a linear regression model into a curve - polynomial regression Dealing with nonlinear relationships using random forests, Working with Unlabeled Data - Clustering Analysis, Grouping objects by similarity using k-means, Organizing clusters as a hierarchical tree, Locating regions of high density via DBSCAN Multilayer Artificial Neural Network and Deep Learning: Modeling complex functions with artificial neural networks, Classifying handwritten digits, Training an artificial neural network, About the convergence in neural networks, A few last words about the neural network implementation, Parallelizing Neural Network Training with Tensor Flow, Tensor Flow and training performance
