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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-from-scratch/
课程评论：没有评论
课程名称：Python与统计学的机器学习 课程概述：本课程专为学生设计，旨在帮助他们学习Python、统计学和机器学习的概念。课程内容经过特别设计，即便是没有技术背景的学生也能够理解复杂的概念。课程包括Python的基本知识，如：变量、函数、Pandas、Numpy、异常处理、网络爬虫、多线程、数据库连接、matplotlib、模块、包、文件、Flask、语法纠错及语音转文本。此外，课程还涉及多个Python项目，包含字谜游戏、贪吃蛇、通讯录和密码生成器。统计学部分涵盖的概念有：推断统计、描述统计、数据类型、总体、集中趋势、离散度的度量、Z-score、最小-最大缩放、协方差、相关性、多重共线性、方差分析、峰度、正态分布、泊松分布、二项分布、假设检验、中心极限定理、自由度、置信区间和P值。 在机器学习方面，课程介绍了重要的算法，包括线性回归、逻辑回归、混淆矩阵、成本矩阵、朴素贝叶斯、K-近邻、决策树算法、随机森林算法、支持向量机、多项式回归、无监督学习、K均值聚类、主成分分析、DBSCAN、线性判别分析等。此外，课程还深入探讨了机器学习模型的开发与部署，帮助用户从零开始搭建和部署模型。
This course is specifically designed for students to learn the concepts in Python, Statistics and Maching Learning. We have tailored this curriculum so that even non-technical students can opt this course and understand the complex concepts. This course includes concepts in Python such as: Variables, functions,Pandas, Numpy, exception handling, web scraping, multithreading,connecting to database, matplotlib, modules, packages,files, flask,grammer correction and speech to text conversion. Projects in Python such as Hangman, Snake Game, Phonebook and Password Generator. For Statistics it includes concepts such as Inferential statistics, Descriptive statistics,data types, population, Central Tendencies, Measures of Dispersion,Z-score, Min-max scaling, Co-variance, Correlation, Multi-collinearity, Anova, Kurtosis,Normal Distribution, Poisson Distribution,Bionominal Distribution,Hypothesis Testing, Central Limit Theorem, Degrees Of Freedom, Confidence Interval, P-value.It also covers important Machine Learning algorithms such as Linear Regression, Logistic Regression,Confusion Matrix, Cost Matrix, Naive Bayes, K-Nearest Neighbors, Decision Tree Algorithm, Random Forest Algorithm,Support Vector Machine, Polynomial Regression, Unsupervised Learning, K-Means Clustering, Principal Component Analysis, DBSCAN, Linear Discriminant Analysis, Linear regression, Logistic Regression, Naive Bayes, KNN, Decision Tree, Support Vector Machine, K means Clustering, Principal Component Analysis, Hierarchical Clustering and Docker for Machine Learning. We have also included 'Deployment of Machine Learning' as one of the section so that user can learn to built the model from scratch and deploy it on its own.
