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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-colab/
课程评论：没有评论
课程名称：《机器学习的数学与编程课程》 课程概述： 《综合性Python机器学习课程》是一门内容丰富且独特的课程。机器学习在当今社会已经引发了一场革命，但要掌握机器学习，首先必须理解其背后的数学原理。本课程旨在帮助学习者从基础到高级概念深入理解机器学习的各个方面。 课程模块集中于以下内容： 1. Python编程基础及其在机器学习中的重要编程构造。 2. 机器学习中的数学公式及算法的详细讲解，着重于数学推导，帮助学生掌握算法的“白箱”视角。 3. 涉及pandas、sklearn、scipy、seaborn和matplotlib等工具，帮助学生有效处理数据并构建模型。 4. 深入讲解损失函数，如交叉熵（Cross Categorical Entropy）、稀疏交叉熵（Sparse Categorical Cross Entropy），使用TensorFlow进行实践。 5. 机器学习算法的实现，包括梯度下降算法、限制玻尔兹曼机、感知器、多层感知器、支持向量机、径向基函数、朴素贝叶斯分类器、集成方法、推荐系统等，所有实现均基于Google Colab。 课程还将涵盖： - 统计学在数据分析中的各个组成要素的应用。 - 数据的图形化表示，以深入洞察模式。 - 算法的数学分析，消除“黑箱”视角。 - 各类机器学习算法的实践实现。 - 从头构建各种模型，采用先进算法。 - 理解机器学习在研究中的应用。 - 每个模块结束时提供小测验，以巩固学习效果。 最后，祝愿每位学习者在前进的道路上努力学习，取得佳绩！
This course of "A Comprehensive Course on Machine Learning using python" is a very comprehensive and unique course in itself. Machine Learning is a revolution now days but we cannot master machine learning without getting the mathematical insight, and this course is designed for the same. Our course starts from very basic to advance concepts of machine learning. We have divided the course into different modules which start from the introduction of python its programming basic and important programming constructs which are extensively used in ML programming. The mathematics involved in Machine learning is normally being not discussed and being left out in , but in our course we have put lot of emphasis in mathematical formulation of algorithms used in ML. We have also designed modules of pandas, sklearn, scipy, seaborn and matplotlib for gearing the students with all important tools which are needed in dealing with data and building the model. The machine learning module focuses on the mathematical derivation on white board through video lectures because we believe that white box view of every concept is very important for becoming an efficient ML expert.In Machine Learning the cost estimation function also called loss functions are very important to understand and in our course we have explained Cross Categorical Entropy, Sparse Categorical Cross Entropy, and other important cost functions using TensorFlow.Concepts like gradient descent algorithm, Restricted Boltzmann Algorithm, Perceptron, Multiple Layer Perceptron, Support Vector Machine, Radial Basis Function , Naïve Bayes Classifier, Ensemble Methods, recommendation system and many more are being implemented with examples using Google Colab.Further I wish best of luck to learners for their sincere efforts in advance…Use of various components of statistics in analyzing dataGraphical representation of data to get deep insight of the patternsMathematical analysis of algorithms to remove the black box viewPractical implementation of all important ML AlgorithmsBuilding various models from scratch using advance algorithmsUnderstanding the use of ML in researchQuiz at the end of each section
