AI & Predictive Analytics with Python

This course is part of Artificial Intelligence with Python: Foundations to Projects Specialization

Instructor: EDUCBA

Access provided by KGiSL Educational Institutions

4 modules

Gain insight into a topic and learn the fundamentals.

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

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.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

13 assignments

Taught in English

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This course is part of the Artificial Intelligence with Python: Foundations to Projects Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
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There are 4 modules in this course

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.

What's included

7 videos3 assignments

7 videos Total 52 minutes

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

3 assignments Total 50 minutes

Graded-Foundations of Predictive Analytics 30 minutes
Getting Started with Predictive Analysis 10 minutes
Boosting & Real-World Prediction 10 minutes

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.

What's included

10 videos3 assignments

10 videos Total 58 minutes

Detecting patterns with Unsupervised Learning 5 minutes
Clustering 7 minutes
Clustering Meanshift 4 minutes
Clustering Meanshift Continues 6 minutes
Affinity Propagation Model 5 minutes
Affinity Propagation Model Continues 5 minutes
Clustering Quality 5 minutes
Program of Clustering Quality 7 minutes
Gaussian Mixture Model 4 minutes
Program of Gaussian Mixture Model 8 minutes

3 assignments Total 50 minutes

Graded-Unsupervised Learning & Pattern Discovery 30 minutes
Exploring Clustering Techniques 10 minutes
Untitled 10 minutes

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.

What's included

20 videos3 assignments

20 videos Total 136 minutes

Classification in Artificial Intelligence 3 minutes
Processing Data 9 minutes
Logistic Regression Classifier 3 minutes
Logistic Regression Classifier Example Using Python 7 minutes
Naive Bayes Classifier and its Examples 11 minutes
Confusion Matrix 4 minutes
Example os Confusion Matrix 6 minutes
Support Vector Machines Classifier(SVM) 5 minutes
SVM Classifier Examplesg 8 minutes
Concept of Logic Programming 11 minutes
Matching the Mathematical Expression 7 minutes
Parsing Family Tree and its Example 9 minutes
Analyzing Geography Logic Programming 5 minutes
Puzzle Solver and its Example 6 minutes
What is Heuristic Search 6 minutes
Local Search Technique 9 minutes
Constraint Satisfaction Problem (CSP) 9 minutes
Region Coloring Problem 5 minutes
Building Maze 7 minutes
Puzzle Solver 9 minutes

3 assignments Total 50 minutes

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

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.