Dive into machine learning with Apache Spark MLLib in this comprehensive tutorial. Learn to implement simple and multiple linear regression models using both Scikit-Learn and Spark MLLib. Set up Apache Spark (PySpark) on Jupyter Notebook and Google Colab, and explore data preprocessing techniques. Master model building, training, and evaluation methods, including MSE, RMSE, and MAE. Gain hands-on experience through step-by-step coding sessions, enhancing your skills in leveraging Spark's distributed computing capabilities for machine learning tasks.
Overview
Syllabus
Simple Linear Regression using Scikit Learn & Spark MLLib | Introduction & Intuition.
Enable Apache Spark(Pyspark) to run on Jupyter Notebook - Part 1 | Install Spark on Jupyter Notebook.
Enable Apache Spark(Pyspark) to run on Jupyter Notebook - Part 2 | Install Spark on Jupyter Notebook.
Run PySpark on Google Colab for FREE! | PySpark on Jupyter.
Simple Linear Regression using Spark MLLib | Introduction.
Simple Linear Regression using Spark MLLib | Data Preprocessing.
Simple Linear Regression using Spark MLLib | Build Train & Evaluate Model.
Simple Linear Regression | Scikit Learn & Spark MLLib | Model Evaluation Techniques - Part 1.
Simple Linear Regression | MSE RMSE & MAE | Model Evaluation Techniques - Part 2.
Multiple Linear Regression using Scikit Learn | Introduction & Intuition.
Multiple Linear Regression using Spark(PySpark) MLLib | Coding Part - 1.
Multiple Linear Regression using Spark(PySpark) MLLib | Coding Part - 2.
Multiple Linear Regression using Spark(PySpark) MLLib | Coding Part - 3.
Taught by
The AI University
