一种基于Python的机器学习数学方法

在本课程中，机器学习的数学概念将用python在Google Colab中教授给学习者

你会学到什么
深入了解建立ML模型背后的数学知识
如何准备数据以输入模型
支持向量机及其核的深入分析
机器学习中集成方法的概念
利用机器学习的概念构建推荐系统
建筑推荐系统
有线电视新闻网模型的实现
时尚MNIST的实现
递归神经网络
在每个部分结束时进行测验，测试您所学的概念
自然语言处理
主动学习

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2 Ch
语言：英语+中英文字幕（云桥CG资源站 机译） |时长:117节课(31h 12m) |大小解压后:19.7 GB


要求
没有先决条件，只有学习的意愿

描述
这门机器学习的数学方法课程本身就是一门非常全面和独特的课程。机器学习现在是一场革命，但是如果没有数学洞察力，我们就不能掌握机器学习，而这门课程也是为机器学习而设计的。我们的课程从最基本的开始，以推进机器学习的概念。我们将课程分为不同的模块，从介绍python开始，python是一种编程基础和重要的编程构造，在ML编程中广泛使用。我们还设计了熊猫、sklearn、scipy、seaborn和matplotlib模块，为学生提供处理数据和构建模型所需的所有重要工具。机器学习模块侧重于通过视频讲座在白板上进行数学推导，因为我们相信每个概念的白盒视图对于成为高效的ML专家非常重要。

梯度下降算法、受限玻尔兹曼算法、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、推荐系统等概念正在使用谷歌Colab通过示例实现。

此外，我祝愿学习者好运，因为他们提前做出了真诚的努力…

在分析数据时使用各种统计成分

数据的图形表示，以深入了解模式

消除黑盒视图算法的数学分析

所有重要最大似然算法的实际实现

使用高级算法从头开始构建各种模型

了解ML在研究中的应用

每个部分结束时的测验

这门课是给谁的
本科生、想学习python和机器学习以及数学概念的毕业生


A Mathematical Approach of Machine Learning using Python

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 117 lectures (31h 12m) | Size: 18.6 GB

In this course the mathematical concepts of machine learning will be taught to learners, with python in Google Colab

What you’ll learn
In depth knowledge of mathematics behind building ML models
How to prepare data for feeding into models
In depth analysis of support vector machines and their kernels
Concepts of Ensemble methods in machine learning
Building Recommendation system by using concepts of machine learning
Building Recommendation System
Implementation of CNN models
Implementation of Fashion MNIST
Recurrent Neural network
Quiz at the end of each Section to test the concepts you have learned
Natural Language Processing
Active Learning

Requirements
No Prerequisites, only will to learn

Description
This course of mathematical approach to machine learning 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. 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.

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 data

Graphical representation of data to get deep insight of the patterns

Mathematical analysis of algorithms to remove the black box view

Practical implementation of all important ML Algorithms

Building various models from scratch using advance algorithms

Understanding the use of ML in research

Quiz at the end of each section

Who this course is for
undergraduates, graduates who want to learn python and machine learning along with their mathematical concepts
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