Fundamentals of Machine Learning 2022

这是机器学习的入门课程。该课程将涵盖广泛的主题，从处理数据集到模型交付逐步教您。本课程假定学生没有先验知识。但是，一些 Python 编程方面的先前培训和一些基本的微积分知识肯定对本课程有所帮助。我们的期望是为您提供与在受认可的本科大学计算机科学课程的机器学习或人工智能入门课程中提供的相同的知识和培训。

课程由尹一巧教授，课程材料由拥有5年以上行业经验的优秀导师团队提供。所有讲师均来自常春藤盟校背景，每个人都渴望与您分享他们对行业的了解。

该课程有以下主题

介绍

统计学习基础

线性回归

分类

采样和引导

模型选择和正则化

超越线性

基于树的方法

支持向量机

深度学习

无监督学习

分类指标

课程由3个部分组成

讲座系列 <= 每章都有其指定的讲座。讲座将介绍模型的技术组件，以帮助学生为具有数学背景的学生做好准备。

实验课 <= 每个实验课只涵盖一个主题。实验课是对章节和讲座视频的补充。

Python notebooks <= 本课程为学生提供可下载的python notebooks，以确保学生具备技术知识并可以自行部署项目。

Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.91 GB | Duration: 8h 40m

This course will start your career in data science.

What you’ll learn
Learn about the fundamental principles of machine learning
Build customized models to use in handle different data science projects
Build customized Deep Learning models to start your own data science career
Start your data science career and connect with the tutor in industry

Requirements
No prior mathematical or programming knowledge required. Some python programming experience is helpful.
Description
This is an introduction course of machine learning. The course will cover a wide range of topics to teach you step by step from handling a dataset to model delivery. The course assumes no prior knowledge of the students. However, some prior training in python programming and some basic calculus knowledge is definitely helpful for the course. The expectation is to provide you the same knowledge and training as that is provided in an intro Machine Learning or Artificial Intelligence course at a credited undergraduate university computer science program.

The course is taught by Yiqiao Yin, and the course materials are provided by a team of amazing instructors with 5+ years of industry experience. All instructors come from Ivy League background and everyone is eager to share with you what they know about the industry.

The course has the following topics

Introduction

Basics in Statistical Learning

Linear Regression

Clasification

Sampling and Bootstrap

Model Selection & Regularization

Going Beyond Linearity

Tree-based Method

Support Vector Machine

Deep Learning

Unsupervised Learning

Classification Metrics

The course is composed of 3 sections

Lecture series <= Each chapter has its designated lecture(s). The lecture walks through the technical component of a model to prepare students with the mathematical background.

Lab sessions <= Each lab session covers one single topic. The lab session is complementary to a chapter as well as a lecture video.

Python notebooks <= This course provides students with downloadable python notebooks to ensure the students are equipped with the technical knowledge and can deploy projects on their own.

Who this course is for
Beginners in python programming, machine learning, and data science.