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

访问权限由 New York State Department of Labor 提供

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

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作业

13 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.

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 Analysis 9分钟
Random Forest and Extremely Random Forest 11分钟
Dealing with Class Imbalance 7分钟
Grid Search 9分钟
Adaboost Regressor 8分钟
Predicting Traffic Using Extremely Random Forest Regressor 2分钟
Traffic Prediction 7分钟

3个作业总计50分钟

Getting Started with Predictive Analysis 10分钟
Boosting & Real-World Prediction 10分钟
Graded-Foundations of Predictive Analytics 30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 Learning 5分钟
Clustering 7分钟
Clustering Meanshift 4分钟
Clustering Meanshift Continues 6分钟
Affinity Propagation Model 5分钟
Affinity Propagation Model Continues 5分钟
Clustering Quality 5分钟
Program of Clustering Quality 7分钟
Gaussian Mixture Model 4分钟
Program of Gaussian Mixture Model 8分钟

3个作业总计50分钟

Exploring Clustering Techniques 10分钟
Untitled 10分钟
Graded-Unsupervised Learning & Pattern Discovery 30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 Intelligence 3分钟
Processing Data 9分钟
Logistic Regression Classifier 3分钟
Logistic Regression Classifier Example Using Python 7分钟
Naive Bayes Classifier and its Examples 11分钟
Confusion Matrix 4分钟
Example os Confusion Matrix 6分钟
Support Vector Machines Classifier(SVM) 5分钟
SVM Classifier Examplesg 8分钟
Concept of Logic Programming 11分钟
Matching the Mathematical Expression 7分钟
Parsing Family Tree and its Example 9分钟
Analyzing Geography Logic Programming 5分钟
Puzzle Solver and its Example 6分钟
What is Heuristic Search 6分钟
Local Search Technique 9分钟
Constraint Satisfaction Problem (CSP) 9分钟
Region Coloring Problem 5分钟
Building Maze 7分钟
Puzzle Solver 9分钟

3个作业总计50分钟

Classification with Python 10分钟
Logic Programming & Problem Solving 10分钟
Graded-Supervised Learning & Logic-Based AI 30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.