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所在平台: Udemy |
课程主页: https://www.udemy.com/course/complete-machine-learning-course-with-python/
课程评论：没有评论
【完整Python机器学习课程】 本课程为您提供机器学习和统计模式识别的广泛入门。我们将深入探讨监督学习（包括生成/判别学习、参数/非参数学习、支持向量机）和无监督学习（包括聚类、降维、核方法）。您将学习学习理论，如偏差/方差权衡、VC理论和大间隔。 学完本课程，您将： * 掌握一套完整的机器学习工具集，以应对大多数现实世界的问题。 * 理解各种回归、分类和其他机器学习算法的性能指标（如R-squared, MSE, 准确率, 混淆矩阵等），以及何时使用它们。 * 通过Bagging, Boosting或Stacking技术组合多个模型。 * 利用无监督机器学习算法（如层次聚类、K-means聚类等）深入理解您的数据。 * 在Spyder和各种IDE中进行开发。 * 使用Matplotlib和Seaborn进行有效的视觉沟通。 * 运用特征工程技术来改进算法预测。 * 利用训练/测试集、K折交叉验证和分层K折交叉验证来选择正确的模型，并预测模型在未见过数据上的表现。 * 使用支持向量机（SVM）进行手写识别及其他一般分类问题。 * 使用决策树预测员工流失。 本课程无需机器学习基础。虽然具备一些基本的Python经验会更有帮助，但并非必需。所有代码都将提供，并且讲师会逐行讲解，您还可以获得问答区的友好支持。 如果您想抓住机器学习的浪潮，并获得数据科学家的高薪，那么这门课程就是为您量身定制的！加入我们，成为一名机器学习工程师！
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins);Gain complete machine learning tool sets to tackle most real world problemsUnderstand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix,etc. and when to use them.Combine multiple models with by bagging, boosting or stackingMake use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your dataDevelop in Spyder and various IDECommunicate visually and effectively with Matplotlib and SeabornEngineer new features to improve algorithm predictionsMake use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen dataUse SVM for handwriting recognition, and classification problems in generalUse decision trees to predict staff attritionAnd much much more!No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q & A area.If you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you!Take this course and become a machine learning engineer!
