Section outline

• Select section Day 1 – ML Foundations & Spark Introduction

  Day 1 – ML Foundations & Spark Introduction

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  • ML vs Statistics vs Data Science

  • Data Preprocessing: Encoding, Missing Values, Outliers

  • Python and R for ML: NumPy, Pandas, ggplot2

  • Spark Basics: RDD, DF, SparkR, MLlib

  • Lab: S&P 500 stock data analysis
• Select section Day 2 – Supervised Learning: Regression

  Day 2 – Supervised Learning: Regression

  • Linear, Multiple Linear Regression

  • Ridge, Lasso, ElasticNet, Cross Validation

  • Gradient Boosting for Regression

  • Lab: Power demand prediction, Housing price regression
• Select section Day 3 – Supervised Learning: Classification

  Day 3 – Supervised Learning: Classification

  • Decision Trees, Random Forests

  • Logistic Regression, Support Vector Machines

  • Evaluation: Confusion Matrix, ROC-AUC

  • Lab: Customer segmentation, Credit risk analysis, UCI wine dataset
• Select section Day 4 – Unsupervised Learning & NLP

  Day 4 – Unsupervised Learning & NLP

  • Clustering: K-Means, Hierarchical

  • Feature Engineering and PCA

  • Text Analytics: TF-IDF, POS, Lemmatization, Sentiment Analysis

  • Lab: Movie genre clustering, IMDB comment classification
• Select section Day 5 – Scalable ML & Deployment

  Day 5 – Scalable ML & Deployment

  • Spark MLlib & ML Pipelines

  • Saving and Serving Models

  • Optional: PredictionIO for streaming models

  • Lab: Stack Overflow dataset processing and community detection