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

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3个模块

深入了解一个主题并学习基础知识。

12 条评论

9 小时完成

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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。

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

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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.

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 Course 8分钟
Python for AI 6分钟
What is Machin Learning 11分钟
Data Processing Effort 9分钟
What is Meaning of Bias 10分钟
Bias vs Variance Tradeoff 8分钟
Model Evolution 11分钟
Scikit Learn 13分钟

3个作业总计50分钟

Introduction to AI and Python 10分钟
Bias, Variance, and Model Evolution 10分钟
Graded - Foundations of AI with Python 30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

12个视频4个作业

12个视频总计111分钟

Loading the Data 11分钟
Checking the Visualization 14分钟
Predict 9分钟
Data Values 8分钟
Applying Dimensionality Reduction 10分钟
Model Selection 10分钟
Neighbors Classifier 10分钟
Accuracy of Classifier 9分钟
ML Classification Hindson 7分钟
Statistical Analysis of the Dataset 6分钟
Import Label Encoder 9分钟
Accuracy Score 7分钟

4个作业总计60分钟

Data Preparation and Visualization 10分钟
Feature Engineering and Model Building 10分钟
Evaluating Classifiers and Datasets 10分钟
Graded - Data Handling and Machine Learning Models 30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

9个视频4个作业

9个视频总计79分钟

Multilayer Perceptron 7分钟
Multilayer Perceptron Continued 8分钟
Number of Clusters 10分钟
Multiple Method 10分钟
Keras-Pytorch and Tensorflow 10分钟
Working on Jupyter Notebook 11分钟
Binary Classification 12分钟
Use Markdown Headings 6分钟
Pyplot 6分钟

4个作业总计60分钟

Neural Networks with Perceptrons 10分钟
Ensemble Methods and Frameworks 10分钟
Classification, Documentation, and Visualization 10分钟
Graded - Deep Learning and Practical AI Applications 30分钟

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位教师

授课教师评分

(8个评价)

EDUCBA

1,050 门课程 244,857 名学生

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EDUCBA

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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.

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 19, 2026审阅

A well-paced course that keeps learners motivated from start to finish.

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