Machine Learning: Theory and Hands-on Practice with Python 专项课程

Machine Learning: Theory and Hands-on Practice with Python 专项课程

Develop Foundational Machine Learning Skills. Add Supervised, Unsupervised, and Deep Learning techniques to your Data Analysis toolkit.

位教师：Daniel E. Acuna

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3 门课程系列

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来自此计划中课程的 10 条评论

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4 周完成

在 10 小时一周

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3 门课程系列

深入学习学科知识

来自此计划中课程的 10 条评论

中级等级

推荐体验

4 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 and explain the core paradigms of machine learning and deep learning.
Build, evaluate, and interpret predictive and exploratory models.
Apply advanced modeling techniques to complex, high-dimensional, and unstructured data.
Make informed, industry-relevant modeling decisions grounded in best practices and ethics.

您将获得的技能

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授课语言：英语（English）

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专业化 - 3门课程系列

Machine Learning: Theory and Hands-on Practice with Python provides a comprehensive foundation in modern machine learning, spanning predictive modeling, unsupervised learning and visualization, and neural network–based approaches. From building and evaluating interpretable regression and classification models, to uncovering structure in unlabeled data, and ultimately training and applying deep learning architectures, you'll develop industry-relevant skills to understand, apply, and critically assess machine learning techniques used in real-world software engineering and AI systems.

This specialization can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS), Master of Science in Artificial Intelligence (MS-AI), and Master of Science in Data Science (MS-DS) degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:

MS in Artificial Intelligence: https://hua.dididi.sbs/degrees/ms-artificial-intelligence-boulder

MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

MS in Data Science: https://hua.dididi.sbs/degrees/master-of-science-data-science-boulder

应用的学习项目

Across these courses, you’ll build hands-on expertise in the data analysis and computational foundations of modern AI through mathematically grounded instruction, applied coding assignments, and comprehensive assessments preparing you for real-world machine learning applications.

Introduction to Machine Learning: Supervised Learning

第 1 门课程 23小时

您将学到什么

Explain and apply the core concepts of supervised learning.
Build, interpret, and evaluate predictive models for regression and classification.
Assess model reliability and improve generalization using validation and regularization techniques.
Apply tree-based and ensemble methods to capture complex relationships in data.

您将获得的技能

类别：Scikit Learn (Machine Learning Library)

类别：Data Preprocessing

Introduction to Machine Learning: Unsupervised Learning

第 2 门课程 15小时

您将学到什么

Explain the goals, challenges, and appropriate use cases of unsupervised learning.
Apply dimensionality reduction techniques to analyze and visualize high-dimensional data.
Discover and interpret structure in data using clustering methods.
Address missing data and recommender system problems using matrix completion techniques.

您将获得的技能

类别：Model Evaluation

类别：Feature Engineering

类别：Statistical Methods

类别：Algorithms

Introduction to Deep Learning

第 3 门课程 18小时

您将学到什么

Explain the mathematical foundations of neural networks and how they learn from data.
Train and regularize deep neural networks for effective generalization.
Design and apply specialized neural network architectures for images and sequences.
Apply transformer-based and multimodal models to real-world scenarios.