Big Data Analytics Using Artificial Intelligence and Machine Learning
August 8, 2026
Day 1: Introduction to Big Data and Artificial Intelligence
Objectives:
- Introduce participants to the concept of big data and its importance in analysis.
- Provide a theoretical overview of AI and Machine Learning techniques.
- Review practical applications of AI in big data analysis.
Topics:
- The Concept of Big Data
- Definition and dimensions of big data (volume, velocity, variety).
- Challenges faced by organizations in dealing with big data.
- Methods for storing and organizing big data.
- Introduction to Artificial Intelligence and Machine Learning
- The difference between AI, Machine Learning, and Deep Learning.
- Fundamentals of machine learning and training models using data.
- Practical business applications of AI in big data analysis.
- Practical Applications of AI in Big Data Analysis
- Using AI to extract insights from big data.
- Enhancing business decisions through data analysis using AI.
Day 2: Machine Learning Techniques for Big Data Analysis
Objectives:
- Learn the different machine learning methods used in big data analysis.
- Explore supervised and unsupervised learning techniques.
- Understand how to select appropriate models for big data analysis.
Topics:
- Supervised Machine Learning Techniques
- How to apply classification and regression techniques.
- Practical examples of using supervised techniques for data analysis.
- Methods for evaluating supervised models.
- Unsupervised Machine Learning Techniques
- Clustering and multivariate analysis techniques.
- Using clustering to identify patterns within big data.
- Practical examples of unsupervised applications.
- Choosing the Right Models for Big Data Analysis
- How to determine the most appropriate model based on data type.
- Advantages and disadvantages of different machine learning models.
Day 3: Practical Applications and Big Data Analytics Tools Using AI
Objectives:
- Apply AI techniques to build analytical models for big data.
- Explore programming tools and applications used for big data analysis.
- Learn how to evaluate and improve AI-based models.
Topics:
- Programming Tools and Big Data Analytics
- Data analysis tools such as Python and R.
- Available programming libraries for data analysis like Pandas and Scikit-Learn.
- Using Hadoop and Spark for big data processing.
- Building Models and Analyzing Data Using AI
- Applying models to big data using AI.
- Learning how to build predictive models and analyze future trends.
- Evaluating and Improving Models
- How to evaluate model effectiveness using performance metrics (e.g., accuracy, recall, F1-Score).
- Methods for optimizing models to achieve the best results.