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