AI-Powered Predictive Analysis: Advanced Methods and Tools

AI-Powered Predictive Analysis: Advanced Methods and Tools, available at $34.99, has an average rating of 4.35, with 59 lectures, based on 189 reviews, and has 66654 subscribers.

You will learn about Advanced techniques in predictive analysis using artificial intelligence Implementation of algorithms like Random Forest, Adaboost Regressor, and Gaussian Mixture Model Handling class imbalance and optimizing models using Grid Search Detecting patterns with unsupervised learning techniques such as clustering and affinity propagation Utilizing classifiers like Logistic Regression, Naive Bayes, and Support Vector Machines for classification tasks Logic programming concepts and applications for problem-solving Heuristic search methods and their applications in solving complex problems Natural language processing techniques including tokenization, stemming, lemmatization, and named entity recognition Understanding and building context-free grammars, recursive descent parsing, and shift-reduce parsing Application of predictive analysis in various domains for making informed decisions and predictions This course is ideal for individuals who are Data scientists and analysts seeking to enhance their predictive modeling skills or Software engineers interested in learning advanced techniques in artificial intelligence for predictive analysis or Professionals working in industries such as finance, healthcare, marketing, and e-commerce where predictive analysis is crucial for decision-making or Students and researchers looking to deepen their understanding of predictive modeling and its applications in real-world scenarios It is particularly useful for Data scientists and analysts seeking to enhance their predictive modeling skills or Software engineers interested in learning advanced techniques in artificial intelligence for predictive analysis or Professionals working in industries such as finance, healthcare, marketing, and e-commerce where predictive analysis is crucial for decision-making or Students and researchers looking to deepen their understanding of predictive modeling and its applications in real-world scenarios.

Enroll now: AI-Powered Predictive Analysis: Advanced Methods and Tools

Summary

Title: AI-Powered Predictive Analysis: Advanced Methods and Tools

Price: $34.99

Average Rating: 4.35

Number of Lectures: 59

Number of Published Lectures: 59

Number of Curriculum Items: 59

Number of Published Curriculum Objects: 59

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Advanced techniques in predictive analysis using artificial intelligence
Implementation of algorithms like Random Forest, Adaboost Regressor, and Gaussian Mixture Model
Handling class imbalance and optimizing models using Grid Search
Detecting patterns with unsupervised learning techniques such as clustering and affinity propagation
Utilizing classifiers like Logistic Regression, Naive Bayes, and Support Vector Machines for classification tasks
Logic programming concepts and applications for problem-solving
Heuristic search methods and their applications in solving complex problems
Natural language processing techniques including tokenization, stemming, lemmatization, and named entity recognition
Understanding and building context-free grammars, recursive descent parsing, and shift-reduce parsing
Application of predictive analysis in various domains for making informed decisions and predictions

Who Should Attend

Data scientists and analysts seeking to enhance their predictive modeling skills
Software engineers interested in learning advanced techniques in artificial intelligence for predictive analysis
Professionals working in industries such as finance, healthcare, marketing, and e-commerce where predictive analysis is crucial for decision-making
Students and researchers looking to deepen their understanding of predictive modeling and its applications in real-world scenarios

Target Audiences

Data scientists and analysts seeking to enhance their predictive modeling skills
Software engineers interested in learning advanced techniques in artificial intelligence for predictive analysis
Professionals working in industries such as finance, healthcare, marketing, and e-commerce where predictive analysis is crucial for decision-making
Students and researchers looking to deepen their understanding of predictive modeling and its applications in real-world scenarios

Welcome to the comprehensive course on Predictive Analysis and Machine Learning Techniques! In this course, you will embark on a journey through various aspects of predictive analysis, from fundamental concepts to advanced machine learning algorithms. Whether you’re a beginner or an experienced data scientist, this course is designed to provide you with the knowledge and skills needed to tackle real-world predictive modeling challenges.

Through a combination of theoretical explanations, hands-on coding exercises, and practical examples, you will gain a deep understanding of predictive analysis techniques and their applications. By the end of this course, you’ll be equipped with the tools to build predictive models, evaluate their performance, and extract meaningful insights from data.

Join us as we explore the fascinating world of predictive analysis and unleash the power of data to make informed decisions and drive actionable insights!

Section 1: Introduction

This section serves as an introduction to predictive analysis, starting with an overview of Java Netbeans. Students will understand the basics of predictive modeling and explore algorithms like random forest and extremely random forest, laying the groundwork for more advanced topics in subsequent sections.

Section 2: Class Imbalance and Grid Search

Here, students delve into more specialized topics within predictive analysis. They learn techniques for addressing class imbalance in datasets, a common challenge in machine learning. Additionally, they explore grid search, a method for systematically tuning hyperparameters to optimize model performance.

Section 3: Adaboost Regressor

The focus shifts to regression analysis with the Adaboost algorithm. Students understand how Adaboost works and apply it to predict traffic patterns, gaining practical experience in regression modeling.

Section 4: Detecting Patterns with Unsupervised Learning

Unsupervised learning techniques are introduced in this section. Students learn about clustering algorithms and meanshift, which are used for detecting patterns in unlabeled data. Real-world applications and implementations in Python are emphasized.

Section 5: Affinity Propagation Model

The Affinity Propagation Model is explored in detail, offering students insights into another clustering approach. Through examples and demonstrations, students understand how this model works and its strengths in clustering tasks.

Section 6: Clustering Quality

This section focuses on evaluating the quality of clustering results. Students learn various metrics and techniques to assess clustering performance, ensuring they can effectively evaluate and interpret the outcomes of clustering algorithms.

Section 7: Gaussian Mixture Model

The Gaussian Mixture Model is introduced, providing students with another perspective on clustering. They understand the underlying principles of this model and its application in practical machine learning scenarios.

Section 8: Classifiers

Students transition to classification tasks, learning about different types of classifiers such as logistic regression, naive Bayes, and support vector machines. They gain insights into how these algorithms work and practical examples using Python.

Section 9: Logic Programming

Logic programming concepts are covered in this section, offering students a different paradigm for problem-solving. They learn about parsing, analyzing family trees, and solving puzzles using logic programming techniques.

Section 10: Heuristic Search

This section explores heuristic search algorithms, focusing on their role in solving complex problems efficiently. Students learn about local search techniques, constraint satisfaction problems, and maze-building applications.

Section 11: Natural Language Processing

The course concludes with a dive into natural language processing (NLP) techniques. Students learn about tokenization, stemming, lemmatization, and named entity recognition, gaining practical skills for text analysis using the NLTK library in Python.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Predictive Analysis

Lecture 2: Random Forest and Extremely Random Forest

Chapter 2: Class Imbalance and Grid Search

Lecture 1: Dealing with Class Imbalance

Lecture 2: Grid Search

Chapter 3: Adaboost Regressor

Lecture 1: Adaboost Regressor

Lecture 2: Predicting Traffic Using Extremely Random Forest Regressor

Lecture 3: Traffic Prediction

Chapter 4: Detecting patterns with Unsupervised Learning

Lecture 1: Detecting patterns with Unsupervised Learning

Lecture 2: Clustering

Lecture 3: Clustering Meanshift

Lecture 4: Clustering Meanshift Continues

Chapter 5: Affinity Propagation Model

Lecture 1: Affinity Propagation Model

Lecture 2: Affinity Propagation Model Continues

Chapter 6: Clustering Quality

Lecture 1: Clustering Quality

Lecture 2: Program of Clustering Quality

Chapter 7: Gaussian Mixture Model

Lecture 1: Gaussian Mixture Model

Lecture 2: Program of Gaussian Mixture Model

Chapter 8: Classifiers

Lecture 1: Classification in Artificial Intelligence

Lecture 2: Processing Data

Lecture 3: Logistic Regression Classifier

Lecture 4: Logistic Regression Classifier Example Using Python

Lecture 5: Naive Bayes Classifier and its Examples

Lecture 6: Confusion Matrix

Lecture 7: Example os Confusion Matrix

Lecture 8: Support Vector Machines Classifier(SVM)

Lecture 9: SVM Classifier Examples

Chapter 9: Logic Programming

Lecture 1: Concept of Logic Programming

Lecture 2: Matching the Mathematical Expression

Lecture 3: Parsing Family Tree and its Example

Lecture 4: Analyzing Geography Logic Programming

Lecture 5: Puzzle Solver and its Example

Chapter 10: Heuristic Search

Lecture 1: What is Heuristic Search

Lecture 2: Local Search Technique

Lecture 3: Constraint Satisfaction Problem

Lecture 4: Region Coloring Problem

Lecture 5: Building Maze

Lecture 6: Puzzle Solver

Chapter 11: Natural Language Processing

Lecture 1: Natural Language Processing

Lecture 2: Examine Text Using NLTK

Lecture 3: Raw Text Accessing (Tokenization)

Lecture 4: NLP Pipeline and Its Example

Lecture 5: Regular Expression with NLTK

Lecture 6: Stemming

Lecture 7: Lemmatization

Lecture 8: Segmentation

Lecture 9: Segmentation Example

Lecture 10: Segmentation Example Continues

Lecture 11: Information Extraction

Lecture 12: Tag Patterns

Lecture 13: Chunking

Lecture 14: Representation of Chunks

Lecture 15: Chinking

Lecture 16: Chunking wirh Regular Expression

Lecture 17: Named Entity Recognition

Lecture 18: Trees

Lecture 19: Context Free Grammar

Lecture 20: Recursive Descent Parsing

Lecture 21: Recursive Descent Parsing Continues

Lecture 22: Shift Reduce Parsing

Instructors

EDUCBA Bridging the Gap
Learn real world skills online

Rating Distribution

1 stars: 16 votes
2 stars: 19 votes
3 stars: 36 votes
4 stars: 52 votes
5 stars: 66 votes

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