|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-using-python-programming/
课程评论：没有评论
课程名称：使用Python编程的机器学习 课程概述： 欢迎参加机器学习及其在Python 3中的实现课程。机器学习的核心是如何让机器像人类一样学习。该课程建议学员具备Python 3的基础知识，以便更容易掌握实现部分，但这并非必需。本课程涵盖了机器学习的核心概念，包括特征、构建机器学习模型的步骤（数据预处理、模型微调、过拟合、欠拟合、偏差、方差、混淆矩阵以及机器学习模型的性能评估等）。 我们将重点理解多种预处理技术的重要性，如二值化、最小最大缩放器、标准缩放器等。使用scikit-learn库，我们可以用几行代码实现多种机器学习算法。然而，仅仅实现算法并不能帮助我们理解其背后的原理。因此，在本课程中，我们将首先深入理解算法背后的数学和概念，然后再在Python中进行实现。此外，我们还将对算法进行可视化，以增加趣味性。 本课程将讨论的算法包括： 1. 线性回归 2. 逻辑回归 3. 支持向量机 4. KNN分类器 5. KNN回归器 6. 决策树 7. 随机森林分类器 8. 朴素贝叶斯分类器 9. 聚类等 我们将比较所有算法的结果，并进行深入的分析。不要再犹豫，快来加入我们吧！
'Machine Learning is all about how a machine with an artificial intelligence learns like a human being'Welcome to the course on Machine Learning and Implementing it using Python 3. As the title says, this course recommends to have a basic knowledge in Python 3 to grasp the implementation part easily but it is not compulsory. This course has strong content on the core concepts of ML such as it's features, the steps involved in building a ML Model - Data Preprocessing, Finetuning the Model, Overfitting, Underfitting, Bias, Variance, Confusion Matrix and performance measures of a ML Model. We'll understand the importance of many preprocessing techniques such as Binarization, MinMaxScaler, Standard ScalerWe can implement many ML Algorithms in Python using scikit-learn library in a few lines. Can't we? Yet, that won't help us to understand the algorithms. Hence, in this course, we'll first look into understanding the mathematics and concepts behind the algorithms and then, we'll implement the same in Python. We'll also visualize the algorithms in order to make it more interesting. The algorithms that we'll be discussing in this course are:1. Linear Regression2. Logistic Regression3. Support Vector Machines4. KNN Classifier5. KNN Regressor6. Decision Tree7. Random Forest Classifier8. Naive Bayes' Classifier9. ClusteringAnd so on. We'll be comparing the results of all the algorithms and making a good analytical approach. What are you waiting for?
