零基础学好机器学习！本课程系统覆盖数据预处理、特征工程、模型训练及超参数调优，深入解析线性回归、SVM等核心算法，助你掌握AI底层逻辑与实战能力。
原始标题：Machine Learning: From Data to Intelligence
本课程是一门面向零基础学员、科研人员及在职转行人员的经典机器学习（ML）系统化入门与进阶指南。课程全面覆盖从原始数据到智能决策的核心全流程，指导学员彻底掌握数据预处理、特征工程、模型训练、交叉验证、超参数调优及模型部署等标准开发工作流。在算法层面，课程深入剖析了线性回归、支持向量机（SVM）、层次聚类和DBSCAN等核心监督与非监督学习算法，并系统阐述了偏差与方差权衡、过拟合/欠拟合防治等底层调优理论，同时前瞻性地引入了神经网络与深度学习的核心概念，旨在帮助学员夯实AI底层逻辑，具备解决现实商业与科研难题的实战能力。
Published 6/2026
Created by Harisudha Kuresan
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 9 Lectures ( 3h 55m ) | Size: 2.4 GB
Machine Learning
What you’ll learn
⚡ (Undergraduate, Postgraduate, and Research Scholars) who want to build a strong foundation in Machine Learning.
⚡ Beginners with little or no prior knowledge of Machine Learning who want to start their AI journey.
⚡ Working Professionals looking to upskill or transition into Machine Learning, Data Science, or Artificial Intelligence roles.
⚡ Faculty Members and Educators who wish to teach Machine Learning concepts effectively.
Requirements
❗ No prior Machine Learning experience is required; this course is designed for beginners.
Description
Unlock the power of Machine Learning and discover how intelligent systems transform data into meaningful insights and intelligent decisions.Machine Learning: From Data to Intelligence is a comprehensive, beginner-friendly course designed to help you build a strong foundation in Machine Learning while gaining practical experience with real-world applications. The course begins by introducing the fundamentals of Machine Learning and Data Science, followed by the complete Machine Learning workflow, including data preprocessing, feature engineering, model training, evaluation, and deployment concepts. You will learn essential supervised and unsupervised learning algorithms such as Linear Regression, Support Vector Machines (SVM), Hierarchical Clustering, and DBSCAN. You will also explore important concepts including bias and variance, overfitting and underfitting, feature scaling, cross-validation, hyperparameter tuning, performance evaluation metrics, and model selection techniques. The course further introduces neural networks, activation functions, and the fundamentals of deep learning to provide a solid understanding of modern AI systems. Whether you are a student, software developer, data analyst, researcher, educator, or an aspiring Data Scientist, this course provides the knowledge and practical experience required to confidently begin your Machine Learning journey. By the end of the course, you will have a solid foundation in Machine Learning, understand how to transform raw data into actionable intelligence, and be ready to apply these techniques to solve real-world challenges.
Who this course is for
⭐ This course is ideal for anyone who wants to learn Machine Learning through clear explanations, and practical examples, while building a strong foundation for advanced AI and Deep Learning topics.
此处内容需要权限查看
会员免费查看
