Life @ Blue Ocean
عربي

Big Data Analytics Using Artificial Intelligence and Machine Learning

Day 1: Introduction to Big Data and Artificial Intelligence

Objectives:

  1. Introduce participants to the concept of big data and its importance in analysis.
  2. Provide a theoretical overview of AI and Machine Learning techniques.
  3. 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:

  1. Learn the different machine learning methods used in big data analysis.
  2. Explore supervised and unsupervised learning techniques.
  3. 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:

  1. Apply AI techniques to build analytical models for big data.
  2. Explore programming tools and applications used for big data analysis.
  3. 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.