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AI & Predictive Analytics with Python

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

了解更多

4个模块

深入了解一个主题并学习基础知识。

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

1 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

Apply predictive analytics and ML algorithms to real problems.
Analyze clustering, classification, and NLP pipelines in Python.
Construct AI solutions using logic, rules, and search strategies.

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

13 项作业

授课语言：英语（English）

91% of learners achieved a positive career outcome

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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

在注册此课程时，您还会同时注册此专项课程。

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获得可共享的职业证书

该课程共有4个模块

This course provides a comprehensive, hands-on introduction to Artificial Intelligence and Predictive Analytics using Python. Learners will progress from foundational concepts of predictive modeling and ensemble methods to advanced unsupervised clustering techniques like Meanshift, Affinity Propagation, and Gaussian Mixture Models. The course then explores supervised learning algorithms, including Logistic Regression, Naive Bayes, and Support Vector Machines, and transitions into logic programming and problem-solving approaches such as heuristic search, local search, and constraint satisfaction problems.

The final module introduces Natural Language Processing (NLP) with Python and NLTK, covering tokenization, stemming, lemmatization, segmentation, information extraction, chunking, Named Entity Recognition (NER), and grammar-based parsing techniques including Context-Free Grammar, recursive descent parsing, and shift-reduce parsing. By the end of this course, learners will be able to: • Apply predictive analytics and machine learning algorithms to real-world problems. • Analyze clustering, classification, and NLP pipelines to process structured and unstructured data. • Evaluate model performance using metrics such as confusion matrices and clustering quality measures. • Construct logic-based AI solutions using rules, constraints, and search strategies. • Design end-to-end workflows for predictive modeling, text mining, and syntactic parsing. This course is ideal for learners seeking to apply, analyze, and evaluate AI methods for data science, predictive analytics, and natural language processing applications using Python.

单元详情

This module introduces learners to the fundamentals of predictive analytics with Python, focusing on essential machine learning methods used in real-world applications. Learners will begin by exploring the core concepts of predictive analysis, then progress into powerful ensemble algorithms such as Random Forest, Extremely Random Forest, and Adaboost, while addressing practical challenges like class imbalance. The module culminates in applying these models to a real-world case study on traffic prediction, ensuring learners gain both conceptual understanding and hands-on predictive modeling experience.

涵盖的内容

7个视频3个作业

7个视频总计52分钟

Introduction to Predictive Analysis9分钟
Random Forest and Extremely Random Forest11分钟
Dealing with Class Imbalance7分钟
Grid Search9分钟
Adaboost Regressor8分钟
Predicting Traffic Using Extremely Random Forest Regressor2分钟
Traffic Prediction7分钟

3个作业总计50分钟

Graded-Foundations of Predictive Analytics30分钟
Getting Started with Predictive Analysis10分钟
Boosting & Real-World Prediction10分钟

This module explores the power of unsupervised learning techniques in Python for discovering hidden patterns in data. Learners will begin with the foundations of clustering methods such as Meanshift and advance into more sophisticated models like Affinity Propagation and Gaussian Mixture Models. The module emphasizes evaluating clustering quality metrics and applying these techniques in practical programming scenarios. By the end of this module, learners will be able to analyze, implement, and evaluate clustering algorithms for real-world applications in domains like customer segmentation, image processing, and pattern recognition.

涵盖的内容

10个视频3个作业

10个视频总计58分钟

Detecting patterns with Unsupervised Learning5分钟
Clustering7分钟
Clustering Meanshift4分钟
Clustering Meanshift Continues6分钟
Affinity Propagation Model5分钟
Affinity Propagation Model Continues5分钟
Clustering Quality5分钟
Program of Clustering Quality7分钟
Gaussian Mixture Model4分钟
Program of Gaussian Mixture Model8分钟

3个作业总计50分钟

Graded-Unsupervised Learning & Pattern Discovery30分钟
Exploring Clustering Techniques10分钟
Untitled10分钟

This module introduces learners to the fundamentals of supervised learning in Python and explores the integration of logic-based programming for AI problem-solving. The first part focuses on popular classification methods such as logistic regression, Naive Bayes, and Support Vector Machines (SVM), along with practical tools like the confusion matrix for evaluating predictive performance. The second part transitions into symbolic AI through logic programming, covering applications such as family tree reasoning, puzzle solving, heuristic search, local search techniques, and constraint satisfaction problems (CSPs). By the end of this module, learners will gain the ability to apply classification algorithms, interpret performance metrics, and construct logic-based solutions to real-world AI challenges.

涵盖的内容

20个视频3个作业

20个视频总计136分钟

Classification in Artificial Intelligence3分钟
Processing Data9分钟
Logistic Regression Classifier3分钟
Logistic Regression Classifier Example Using Python7分钟
Naive Bayes Classifier and its Examples11分钟
Confusion Matrix4分钟
Example os Confusion Matrix6分钟
Support Vector Machines Classifier(SVM)5分钟
SVM Classifier Examplesg8分钟
Concept of Logic Programming11分钟
Matching the Mathematical Expression7分钟
Parsing Family Tree and its Example9分钟
Analyzing Geography Logic Programming5分钟
Puzzle Solver and its Example6分钟
What is Heuristic Search6分钟
Local Search Technique9分钟
Constraint Satisfaction Problem (CSP)9分钟
Region Coloring Problem5分钟
Building Maze7分钟
Puzzle Solver9分钟

3个作业总计50分钟

Graded-Supervised Learning & Logic-Based AI30分钟
Classification with Python10分钟
Logic Programming & Problem Solving10分钟

This module provides a practical foundation in Natural Language Processing (NLP) using Python and NLTK. Learners will explore the complete NLP pipeline, from tokenization and text preprocessing to stemming, lemmatization, and segmentation. The module further introduces advanced tasks such as information extraction, chunking, chinking, and Named Entity Recognition (NER). Finally, learners will study parsing techniques using Context-Free Grammar (CFG), recursive descent parsing, and shift-reduce parsing to analyze sentence structure. By the end of this module, learners will be able to apply NLP techniques in Python for text analysis, information extraction, and grammar-based parsing of natural language.