AI-Powered Predictive Analysis: Advanced Methods and Tools

Dive deep into predictive analysis leveraging AI, covering Adaboost, Gaussian Mixture Model, and classification algo.

Created by: EDUCBA Bridging the Gap
Learn real world skills online

What Will I 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

Requirements

• Basic knowledge of Python
• Beginner knowledge of Statistics

Target audience

• 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