Fundamentals of Machine Learning

Fundamentals of Machine Learning

位教师：Packt - Course Instructors

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3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Understand core machine learning techniques such as regression, classification, and decision trees
Gain practical experience in model evaluation through techniques like cross-validation and bootstrap
Explore advanced methods in deep learning and neural networks for solving complex tasks
Apply machine learning models to real-world datasets and interpret their performance

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作业

4 项作业

授课语言：英语（English）

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该课程共有3个模块

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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course offers a comprehensive foundation in machine learning, taking you through both the theoretical and practical aspects of this powerful field. By learning the fundamentals of algorithms, models, and techniques, you will gain the skills to design, implement, and assess machine learning systems effectively. Throughout the course, you'll dive deep into various methods, including regression, classification, decision trees, SVM, deep learning, and more. The course is structured into lectures, hands-on labs, and deep learning-focused modules. It starts with foundational concepts such as statistical learning and progresses to complex models like neural networks and support vector machines. You'll also explore practical tools like Principal Component Analysis (PCA), random forests, and classification metrics, helping you build confidence in both theory and application. Ideal for those new to the field of machine learning, the course assumes no prior experience in programming or data science. However, a basic understanding of algebra and statistics will be beneficial. It's designed for learners at all levels, providing an accessible entry point into machine learning while offering deep technical insights for more experienced students. By the end of the course, you will be able to implement machine learning models, use deep learning techniques, assess model performance, and apply machine learning methods to real-world datasets.

In this module, we will explore the foundational principles of machine learning, from the basics of statistical learning to advanced techniques like decision trees and deep learning. You will learn essential concepts such as linear regression, classification, and the importance of model selection to prevent overfitting. By the end, you will gain a comprehensive understanding of how machine learning works and the tools used to build robust models.

涵盖的内容

14个视频1篇阅读材料

14个视频总计403分钟

Welcome 2分钟
Introduction 9分钟
Basics in Statistical Learning 43分钟
Linear Regression 39分钟
Classification 23分钟
Sampling and Bootstrap 15分钟
Model Selection 35分钟
Going Beyond Linearity 9分钟
Tree-Based Methods – Part 1 37分钟
Tree-Based Methods – Part 2 38分钟
Support Vector Machine (SVM) 22分钟
Deep Learning 58分钟
Unsupervised Learning 52分钟
Classification Metrics 23分钟

1篇阅读材料总计10分钟

Full Course Resources 10分钟

In this module, we will dive into hands-on labs where you will apply theoretical knowledge to solve real-world machine learning problems. You will work with popular algorithms such as linear regression, SVM, and decision trees, experimenting with techniques like PCA for data reduction and building deep learning models like CNNs. By the end, you will be able to build and fine-tune machine learning models to handle diverse datasets.

涵盖的内容

10个视频1个作业

In this module, we will focus on deep learning, specifically Large Language Models (LLMs), and their applications. You will gain practical experience with powerful SDKs like OpenAI and LangChain, learning to build and optimize LLM agents for real-world scenarios. By the end of the module, you will have a deeper understanding of LLMs and be equipped to deploy them effectively using advanced tools and techniques.