AI-ML for Predictive Analytics

AI & ML for Predictive Analysis offers a comprehensive understanding of leveraging artificial intelligence and machine learning for forecasting and decision-making. Participants will explore key concepts, tools, and techniques to analyze data, predict outcomes, and drive innovation, enabling smarter strategies and improved efficiency across diverse industries.

The aim of this course is to empower participants with a strong foundation in AI and ML for predictive analysis. It focuses on equipping learners with practical tools, advanced techniques, and the ability to harness data for accurate forecasting, improved decision-making, and innovative problem-solving across diverse industries and applications.  

• Understand AI & ML Fundamentals: Gain foundational knowledge of artificial intelligence and machine learning concepts.
• Learn Predictive Modeling: Explore techniques to build and implement predictive models for data-driven decision-making.
• Master Data Analysis: Develop skills to preprocess and analyze data for insights and predictions.
• Utilize AI/ML Tools: Familiarize with key tools and frameworks used in AI and ML applications.
• Apply in Real-World Scenarios: Solve industry-specific problems using predictive analytics techniques.
• Enhance Forecasting Accuracy: Improve prediction precision through advanced algorithms and data handling methods.
• Understand Ethical AI Practices: Learn responsible AI practices to ensure ethical and transparent applications.
• Foster Innovation: Enable innovative approaches to optimize processes and strategies through predictive analytics.

Day 1: Foundations of Predictive Analytics in AI Session 1: Introduction to Predictive Analytics and its Importance

• Predictive analytics in research and business
• Key components: data, algorithms, and models

Session 2: Introduction to Key Algorithms

• Regression models (Linear and Logistic Regression)
• Decision Trees and Random Forests for prediction

Day 2: Building Predictive Models with Machine Learning Session 1: Implementing Machine Learning Models for Prediction

• Using Python libraries: Scikit-learn and NumPy, Pandas, Matplotlib/Seaborn
•  Step-by-step model building: training, validation, and testing

Session 2: Evaluation Metrics and Model Optimization

• Hyperparameter tuning and model selection techniques
• Performance metrics: accuracy, Precision, Recall, Confusion Matrix

Day 3: AI Applications in Predictive Analytics Session 1: AI in Healthcare and Research Predictions

• AI Applications in Healthcare
• AI in academic research: predicting trends and outcomes 

Session 2: AI for Business Analytics

• AI in sales forecasting, customer behavior analysis, and risk assessment
• Real-life applications of AI in business

• Professionals in data science or analytics
• Students in computer science, engineering, or data analytics
• Industry practitioners in AI/ML applications
• Entrepreneurs leveraging data-driven insights
• Researchers in predictive modeling
• Individuals with programming and statistics knowledge
• Professionals across diverse industries
• Beginners in AI and predictive analysis