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AI with Python: Apply & Implement ML Models

本课程是 Artificial Intelligence with Python: Foundations to Projects 专项课程的一部分

位教师：EDUCBA

包含在中

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3个模块

深入了解一个主题并学习基础知识。

13 条评论

9 小时完成

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。

13 条评论

9 小时完成

灵活的计划

自行安排学习进度

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.

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作业

11 项作业

授课语言：英语（English）

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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
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该课程共有3个模块

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.

What makes this course unique is its step-by-step structure combining theory with practical coding demonstrations using Jupyter Notebook, ensuring learners can directly apply concepts to real-world problems. Through integrated lessons on documentation and visualization, participants will also learn how to clearly present AI projects. Designed for intermediate-level learners, this course bridges the gap between basic knowledge and advanced AI applications, empowering you to confidently build, test, and refine machine learning and deep learning models.

单元详情

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

8个视频总计75分钟

Introduction to Course8分钟
Python for AI6分钟
What is Machin Learning11分钟
Data Processing Effort9分钟
What is Meaning of Bias10分钟
Bias vs Variance Tradeoff8分钟
Model Evolution11分钟
Scikit Learn13分钟

3个作业总计50分钟

Introduction to AI and Python10分钟
Bias, Variance, and Model Evolution10分钟
Graded - Foundations of AI with Python30分钟

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

13个视频4个作业

13个视频总计121分钟

Loading the Data11分钟
Checking the Visualization14分钟
Predict9分钟
Data Values8分钟
Applying Dimensionality Reduction10分钟
Model Selection10分钟
Neighbors Classifier10分钟
Accuracy of Classifier9分钟
ML Classification Hindson7分钟
Statistical Analysis of the Dataset6分钟
Import Label Encoder9分钟
Accuracy Score7分钟
Number of Clusters10分钟

4个作业总计60分钟

Data Preparation and Visualization10分钟
Feature Engineering and Model Building10分钟
Evaluating Classifiers and Datasets10分钟
Graded - Data Handling and Machine Learning Models30分钟

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

8个视频4个作业

8个视频总计69分钟

Multilayer Perceptron7分钟
Multilayer Perceptron Continued8分钟
Multiple Method10分钟
Keras-Pytorch and Tensorflow10分钟
Working on Jupyter Notebook11分钟
Binary Classification12分钟
Use Markdown Headings6分钟
Pyplot6分钟

4个作业总计60分钟

Neural Networks with Perceptrons10分钟
Ensemble Methods and Frameworks10分钟
Classification, Documentation, and Visualization10分钟
Graded - Deep Learning and Practical AI Applications30分钟

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

授课教师评分

(8个评价)

EDUCBA

1,379 门课程303,698 名学生

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EDUCBA

学生评论

5 stars
76.92%
4 stars
15.38%
3 stars
0%
2 stars
7.69%
1 star
0%

显示 3/13 个

已于 Jan 12, 2026审阅

Very well-designed course with clear explanations and smooth flow throughout.

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.

已于 Jan 8, 2026审阅

A very well-structured course that perfectly combines Python programming with AI fundamentals

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