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
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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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