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Course Description

Big Data & Machine Learning is a comprehensive training program designed to help participants understand how large-scale data is collected, processed, analyzed, and transformed into predictive insights using machine learning models. This course provides a strong foundation in big data concepts, data engineering workflows, and machine learning techniques, enabling participants to apply data-driven decision-making and build intelligent models for real business and operational use cases.

Course Objectives

Upon the successful completion of this course, each participant will be able to:

  • Understand big data concepts, architectures, and real-world applications.
  • Identify the characteristics and challenges of large-scale datasets (Volume, Velocity, Variety, Veracity, Value).
  • Explain the end-to-end data lifecycle from ingestion to analytics.
  • Use tools and workflows for data processing and preparation.
  • Understand machine learning fundamentals and model development process.
  • Apply supervised and unsupervised learning methods.
  • Evaluate model performance using appropriate metrics.
  • Understand how to deploy and operationalize machine learning solutions.
  • Recognize ethical, privacy, and governance issues in big data and AI systems.

Who Should Attend?

This course is designed for data analysts, engineers, IT professionals, business intelligence staff, and professionals who want to build skills in big data processing and machine learning applications.

Course Agenda

Registration

Welcome & IntroductionPre-Test

Day 1: Big Data Fundamentals & Data Ecosystem

  • Introduction to Big Data and its business value
  • Traditional data vs big data
  • The 5Vs of Big Data (Volume, Velocity, Variety, Veracity, Value)
  • Data types: structured, semi-structured, unstructured
  • Big Data ecosystem overview
  • Data sources: IoT, social media, business systems, logs
  • Introduction to data pipelines and ETL/ELT
  • Basics of distributed computing
  • Workshop Activity: Big data use case brainstorming by industry
Day 2: Big Data Architecture & Data Engineering Concepts

  • Big data architecture overview
  • Data lakes vs data warehouses vs data marts
  • Batch processing vs stream processing
  • Introduction to Hadoop ecosystem (HDFS, MapReduce, YARN)
  • Apache Spark fundamentals
  • Introduction to NoSQL databases (MongoDB, Cassandra, HBase)
  • Data ingestion tools overview (Kafka, Flume, Sqoop)
  • Cloud big data platforms (AWS, Azure, GCP overview)

Day 3: Data Preparation, Feature Engineering & Exploratory Data Analysis (EDA)

  • Importance of data quality and data cleaning 
  • Data preprocessing techniques:
          Missing values handling

         Outliers detection

         Normalization and standardization

  • Exploratory Data Analysis (EDA) techniques
  • Data visualization for insights
  • Feature selection and feature engineering concepts
  • Encoding categorical variables
  • Data splitting (train/test/validation)
  • Introduction to Python for data analytics (Pandas, NumPy basics)
Day 4: Machine Learning Concepts & Model Building

  • Introduction to Machine Learning (ML) and AI
  • ML workflow and model lifecycle
  • Supervised learning:
          Regression models (Linear Regression)

          Classification models (Logistic Regression, Decision Trees)

  • Unsupervised learning:

          Clustering (K-Means)

          Dimensionality reduction (PCA concept)

    • Model training process
    • Bias-variance tradeoff
    • Overfitting and underfitting
    • Hyperparameter tuning basics
    Day 5: Model Evaluation, Deployment & Real-World Applications

    • Model evaluation metrics:
               Accuracy, Precision, Recall, F1 Score

              Confusion Matrix

              ROC Curve and AUC concept

              Regression metrics (MAE, MSE, RMSE)

    • Cross-validation basics
    • Model interpretability (feature importance, explainable AI overview)
    • Introduction to ML deployment concepts:

              APIs and production models

              Batch scoring vs real-time scoring

    • MLOps overview (monitoring, retraining, version control)
    • Ethical AI, data privacy, bias, and governance

    Post Test

    End of the Course

    Assessment Methodology

    All courses conducted by EdTech will begin with a Pre-evaluation and end with a Post-evaluation. The instructor will evaluate the knowledge and skills of the participants according to the feedback given by participants. This will help to recognize the benefits and the level of knowledge gained by participants through the course.

    Training Methodology

    Facilitated by a highly qualified specialist, who has extensive knowledge and experience; this program will be conducted using extensively interactive methods, encouraging participants to share their own experiences and apply the program material to real-life work situations in order to stimulate group discussions and improve the efficiency of the subject coverage.

    Percentages of the total course hour classification are:

    • ​40% Theoretical lectures, Concepts and approach
    • 20% Motivation to develop individual skill and Techniques
    • 20% Case Studies and Practical Exercises
    • 20% Topic General Discussions and interaction

    Course Manual

    Participants will be provided with comprehensive presentation material as reference manual. This presentation material is a compilation of core valuable information, references, presentation methods and inspiring reading which will be used as a part of the material guide.

    Course Certificate

    At the completion of the course, all participants who successfully accomplished the required contact hours will receive an EdTech Training Participation Certificate as a testimony to their commitment to professional development and further education.

    Why Edtech ?

    • Industry Experienced; Internationally Qualified Trainers
    • Hands-on Practical Sessions & Assignments
    • Intensive Study materials
    • Flexible Schedules
    • Realistic training methodology
    • High-Quality Training in Affordable Course Fees
    • Achievement Certificate, as approved by the Ministry of Education (Abu Dhabi Center for Technical and Vocational Education Training - ACTVET), HABC, AWS, IAOSHE, SHRM, etc.