What will you in the Machine Learning With Big Data Course
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Understand the fundamentals of machine learning and how it scales to big data.
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Explore data using statistical summaries and visualizations.
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Prepare data through cleaning, feature engineering, and transformation techniques.
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Build and evaluate classification models using algorithms like Decision Trees, Naïve Bayes, and k-NN.
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Implement and scale machine learning pipelines using Apache Spark and KNIME.
Program Overview
1. Welcome
Duration: 30 minutes
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Course introduction and overview of tools (KNIME and Spark).
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Context of big data and machine learning convergence.
2. Introduction to Machine Learning
Duration: 2.5 hours
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Machine learning cycle: from problem framing to deployment.
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Supervised vs. unsupervised learning approaches.
3. Data Exploration
Duration: 2 hours
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Understanding variables, distributions, and data types.
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Use of summary statistics and visualization tools.
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Data inspection through KNIME and Spark interfaces.
4. Data Preparation
Duration: 2.5 hours
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Addressing missing values, normalization, and outlier detection.
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Feature transformation and selection for modeling efficiency.
5. Classification Techniques
Duration: 3 hours
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Application of classification algorithms including k-Nearest Neighbors, Naïve Bayes, and Decision Trees.
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Training and testing workflows in both Spark and KNIME.
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Model parameter tuning and validation.
6. Model Evaluation and Course Wrap-Up
Duration: 3.5 hours
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Evaluation metrics: accuracy, precision, recall, F1-score.
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Introduction to regression, clustering, and association analysis.
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Final summary and next steps in the machine learning journey.
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Job Outlook
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Machine Learning Engineers: Learn scalable model deployment using Spark.
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Data Scientists: Apply end-to-end machine learning workflows to massive datasets.
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BI & Analytics Professionals: Build predictive models for business insights.
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Software Developers: Gain practical knowledge in integrating ML algorithms into production systems.
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Researchers & Students: Strengthen foundational understanding for academic or applied work in AI.
