Course Description

AI-Powered Predictive Analysis: Advanced Methods and Tools

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.

Registration Link

Provider

udemy

Target

  • Beginners in Data Science
  • Experienced Data Scientists
  • Data Analysts
  • Machine Learning Practitioners
  • Students pursuing a career in AI/ML

Sector

  • Data Science
  • Technology and IT
  • Business Intelligence
  • Education and Training
  • Research and Development

Area

  • Predictive Analysis
  • Machine Learning Algorithms
  • Data Modeling
  • Unsupervised and Supervised Learning
  • Natural Language Processing (NLP)

Method

Online

Certification

Yes

Duration

6.5 hours on-demand video

Assessment

No

Cost

19.99

Learning Outcomes

  • 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

Learning Content

  • Introduction
  • Class Imbalance and Grid Search
  • Adaboost Regressor
  • Detecting Patterns with Unsupervised Learning
  • Affinity Propagation Model
  • Clustering Quality
  • Gaussian Mixture Model
  • Classifiers
  • Logic Programming
  • Heuristic Search
  • Natural Language Processing