Machine Learning With Big Data Course Syllabus

Overview: This course provides a hands-on introduction to machine learning with big data, designed for beginners seeking practical skills in scalable machine learning. Over approximately 15 hours, learners will progress through foundational concepts, data exploration and preparation, model building, and evaluation using industry-standard tools like Apache Spark and KNIME. The curriculum balances theory with real-world application, guiding students from data inspection to deploying machine learning workflows on large datasets.

Module 1: Welcome

Estimated time: 0.5 hours

• Course introduction and learning objectives
• Overview of tools: KNIME and Apache Spark
• Context of machine learning in big data environments

Module 2: Introduction to Machine Learning

Estimated time: 2.5 hours

• The machine learning cycle: from problem framing to deployment
• Supervised vs. unsupervised learning approaches
• Types of machine learning problems: classification, regression, clustering
• Real-world applications of machine learning at scale

Module 3: Data Exploration

Estimated time: 2 hours

• Understanding variables, data types, and distributions
• Using summary statistics for data inspection
• Data visualization techniques for exploratory analysis
• Exploring datasets using KNIME and Spark interfaces

Module 4: Data Preparation

Estimated time: 2.5 hours

• Handling missing values and data imputation
• Normalization and scaling techniques
• Outlier detection and treatment
• Feature transformation and selection for modeling

Module 5: Classification Techniques

Estimated time: 3 hours

• Introduction to classification algorithms: Decision Trees, Naïve Bayes, k-NN
• Training and testing models in Spark and KNIME
• Model parameter tuning and cross-validation
• Building scalable classification pipelines

Module 6: Model Evaluation and Course Wrap-Up

Estimated time: 3.5 hours

• Evaluation metrics: accuracy, precision, recall, F1-score
• Introduction to regression, clustering, and association analysis
• Comparing model performance across tools
• Final summary and next steps in machine learning journey

Prerequisites

• Basic programming experience (Python or R helpful)
• Familiarity with fundamental statistics concepts
• Access to a computer with Spark and KNIME setup capability

What You'll Be Able to Do After

• Understand the fundamentals of machine learning in big data contexts
• Explore and visualize large datasets using statistical methods
• Prepare real-world data for machine learning through cleaning and transformation
• Build and evaluate classification models using Spark and KNIME
• Apply scalable machine learning workflows to industry problems