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所在平台: Udemy |
课程主页: https://www.udemy.com/course/intermediate-machine-learning/
课程评论：没有评论
**课程名称：** 中级机器学习 **课程概述：** 本课程旨在为学生全面介绍机器学习的各个方面，涵盖从数据管道到各类和子领域模型。课程将深入探讨数据收集、数据分析与解读、数据可视化以及数据清洗和预处理等数据管道的关键环节。同时，课程还将说明不同类型数据（如表格数据、图像、文本和时间序列数据）在数据管道中的差异。 随后，课程将重点介绍主流的监督学习和无监督学习模型，以及一些深度学习架构。简要涉及半监督学习和强化学习。在讲解具体模型时，课程将侧重于模型的核心思想、模型间的关键区别、以及各自的优缺点，但不会提供过于深入的数学推导，仅会触及一些影响模型工作原理的数学概念。 除了数据管道和模型类型，本课程还将教授学生如何构建基础到中级的机器学习解决方案。内容包括：Python 中常用的机器学习框架（Scikit-learn, PyTorch, TensorFlow, Jax）、机器学习项目设计步骤、如何训练、微调和评估模型以获取鲁棒的性能估计，以及一些将机器学习模型应用于实际数据集的实践案例。 本课程与“机器学习导论”课程存在一定重叠，但将以更详尽的方式深入探讨相关主题，目标是让学生在课程结束后能够独立实现自己的机器学习解决方案。 **重点内容：** * **数据管道：** 数据收集、分析、可视化、清洗和预处理，以及不同数据类型（表格、图像、文本、时间序列）的处理差异。 * **机器学习模型：** * **重点：** 主流监督学习模型、无监督学习模型。 * **次要：** 深度学习架构、半监督学习、强化学习。 * **学习目标：** 模型核心思想、模型间区别、优缺点。 * **机器学习解决方案构建：** * **工具：** Python 机器学习框架（Scikit-learn, PyTorch, TensorFlow, Jax）。 * **流程：** 项目设计、模型训练、微调、评估。 * **实践：** 实际案例分析。 **学习目标：** * 理解机器学习流水线的各个环节。 * 掌握不同类型机器学习模型的基本原理和应用场景。 * 能够运用主流的Python机器学习框架解决实际问题。 * 具备设计、训练、评估和调优机器学习模型的能力。
This course will introduce students to the field of machine learning by providing a broad overview of all of the various aspects of a machine learning pipeline, as well as the various types and subfields of machine learning models. We will explain various aspects of the data pipeline, such as what to consider during data collection, how to analyze and interpret your datasets, how to create meaningful visualizations of your data and how to clean and prepare your datasets for training machine learning models. These discussions will also provide students with insights regarding how the various aspects of the data pipeline changes for different types of data, such as tabular, image, text and time series data. Students will then learn about the various subfields of machine learning, with a particular focus on the most popular supervised and unsupervised machine learning models, as well as a few deep learning architectures. We will also discuss semi-supervised and reinforcement learning to a lesser extent. Lectures regarding specific models will aim to teach students what the core idea behind the models are, what the main differences between the various models are and what is considered to be their pros and cons. We will not provide detailed mathematical explanations regarding these models, but certain discussions provide some insights into aspects of the underlying mathematics that influence how the models work and what problems they are suitable for.Apart from discussing data pipelines and the various types of machine learning models, this course will also provide students with the necessary information to be able to build their own machine learning solutions for basic to intermediate problems. This includes discussions of the popular machine learning frameworks in Python (Scikit-learn, PyTorch, Tensorflow and Jax), the steps that should be considered when designing a machine learning project, how to train, finetune and evaluate machine learning models in a way that will provide robust performance estimations as well as a few practical examples where machine learning models are applied to some demonstrative datasets.There is considerable overlap between our Introduction to Machine Learning course and this course, but we discuss the various topics in more detail in this course with the aim to enable students to be able to implement their own machine learning solutions by the end of the course.
