By the end of this course, learners will be able to analyze datasets, apply machine learning algorithms, evaluate classifiers, and implement deep learning models using Python and its popular frameworks. The course begins with the foundations of AI, covering essential concepts such as Python for AI, bias-variance tradeoff, and model evolution. Learners will then explore data handling, visualization, dimensionality reduction, and classifier evaluation to strengthen practical ML skills. Finally, the course dives into advanced AI with multilayer perceptrons, clustering, ensemble methods, and hands-on practice with TensorFlow, Keras, and PyTorch.
AI with Python: Apply & Implement ML Models
您将学到什么
Analyze datasets and apply key ML algorithms in Python.
Evaluate classifiers and perform dimensionality reduction.
Build deep learning models with TensorFlow, Keras, and PyTorch.
您将获得的技能
- Supervised Learning
- PyTorch (Machine Learning Library)
- Matplotlib
- Dimensionality Reduction
- Python Programming
- Machine Learning
- Data Preprocessing
- Model Evaluation
- Deep Learning
- Applied Machine Learning
- Artificial Neural Networks
- Jupyter
- Keras (Neural Network Library)
- Artificial Intelligence
- Data Cleansing
- Data Manipulation
- Tensorflow
- 技能部分已折叠。显示 9 项技能，共 17 项。
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作业
11 项作业
授课语言：英语（English）
最近已更新！
September 2025
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积累特定领域的专业知识
本课程是 Artificial Intelligence with Python: Foundations to Projects 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
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- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有3个模块
This module builds a strong foundation in Artificial Intelligence by introducing Python’s role in AI, exploring the basics of machine learning, and emphasizing the importance of data processing. Learners will also examine the concepts of bias, variance, and model evolution while gaining hands-on exposure to Scikit-learn, a widely used machine learning library. By the end of this module, learners will be equipped with essential skills to begin building AI solutions confidently.
涵盖的内容
8个视频3个作业
This module focuses on data handling, preprocessing, and visualization to ensure clean and structured datasets. Learners will practice applying dimensionality reduction techniques, model selection strategies, and classifier methods such as KNN. Additionally, the module highlights evaluation metrics, statistical analysis, and encoding methods to improve classification performance. By completing this module, learners will gain practical skills to prepare data effectively and build accurate machine learning models.
涵盖的内容
12个视频4个作业
This module introduces learners to advanced AI techniques, including multilayer perceptrons, clustering, and ensemble methods. It also provides hands-on exposure to popular frameworks like TensorFlow, PyTorch, and Keras within Jupyter Notebook environments. The module concludes with practical applications in binary classification, documentation using Markdown, and visualization with Pyplot, empowering learners to implement deep learning models and present AI projects effectively.
涵盖的内容
9个视频4个作业
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人们为什么选择 Coursera 来帮助自己实现职业发展
Felipe M.
自 2018开始学习的学生
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Jennifer J.
自 2020开始学习的学生
''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'
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自 2021开始学习的学生
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学生评论
- 5 stars
83.33%
- 4 stars
16.66%
- 3 stars
0%
- 2 stars
0%
- 1 star
0%
显示 3/12 个
SM
已于 Feb 1, 2026审阅
Excellent learning experience. The step-by-step approach makes it easy to grasp AI concepts without feeling overwhelmed.
KP
已于 Jan 14, 2026审阅
This course provides a clear and practical understanding of AI and machine learning using Python. The concepts are explained in a simple way, making it easy to apply them in real-world projects.
TS
已于 Jan 19, 2026审阅
A well-paced course that keeps learners motivated from start to finish.
