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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-mlpredictive-analyticsmodeling-interview-prep/
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课程名称：数据科学 ML：预测分析与建模面试准备 概述：欢迎参加这门全面的机器学习和数据科学课程！本课程旨在为新手、机器学习工程师和数据科学家提供技能提升，以及希望快速转型的学生或专业人士。在课程中，我们将深入探讨一系列重要主题： - 混淆矩阵及分析：了解机器学习模型的性能并提高其准确性。 - 数据分析的类型：学习不同类型的数据分析及其在有效预测中的应用。 - 简单线性回归：了解监督学习和非监督学习的区别，以及最佳拟合、最差拟合线和斜率的概念。 - 不同类型的机器学习模型：其优缺点及应用场景。 - 分类：掌握逻辑回归、朴素贝叶斯、聚类及数值LDA等技术，理解距离度量和矩阵中的缩放。 - 精确朴素贝叶斯分类器：学习概率、预测和K近邻算法，理解欧几里得距离、曼哈顿距离和Mahalanobis距离，并通过数值实例加以说明。 - 过拟合与欠拟合：学习K折交叉验证、提前停止、剪枝、决策树、正则化以及偏差-方差权衡等。 每个主题将深入讲解，并配有数值练习和实例以巩固理解。到课程结束时，您将具备扎实的机器学习和数据科学基础，走向成为该领域专家的道路。 无论您是数据科学和机器学习的新手，还是希望提升的资深专业人士，本课程都能满足您的需求，助您成为数据科学分析工程师或机器学习专业人士。加入我们，踏上这段激动人心的旅程，从而迈出在数据科学和机器学习领域更有影响力的第一步！立即注册，开始学习吧！
Welcome to comprehensive course on Machine Learning and Data Science for Everyone! This course is designed for those who are new to the field, as well as Machine Learning Engineers and Data Scientists looking to level up their skills or Students or Profession want to make a swift transition into this field.In this course, we will delve into a wide array of essential topics:Confusion Matrix & Analysis: Understand the performance of your machine learning model and improve its accuracy.Types of Data Analytics: Learn the different types of data analytics and how they can be used to make effective predictions.Simple Linear Regression: Discover the difference between supervised and unsupervised learning, and understand the concept of best fit, worst fit lines, and slopes.Different types of Machine Learning Models their Limitations and Advantages and where to use which and their applicationsClassification: Master techniques such as logistic regression, naive bayes, clustering, and numerical LDA. Understand distance metrics and scalers within matrices.Exact Naive Bayes Classifier: Learn about probability, prediction, and K-nearest neighbors algorithms. Understand Euclidean, Manhattan, and Mahalanobis distances with numerical examples.Overfitting & Underfitting: Learn about K-fold, early stopping, pruning, decision trees, regularization, and the bias-variance tradeoff and More etc.Each topic will be covered in depth, accompanied by numerical exercises and examples to reinforce your understanding. By the end of this course, you will have a solid foundation in machine learning and data science, and be well on your way to becoming an expert in the field.Whether you're a beginner in data science and machine learning, or a seasoned professional looking to level up, this course has something for everyone to become a data science analytics engineer or machine learning professional etc. Join us on this exciting journey and take the first step towards a more impactful future in data science and machine learning!Enroll now and let's get started!
