Getting Started with Machine Learning using Apache Spark
- Created By raju2006
- Last Updated December 26th, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
Master Kubernetes fundamentals and application development patterns in this comprehensive course. Explore key concepts, including Virtual Machines, Containers, and Processes, through hands-on labs. Enhance your expertise in deploying applications with Kubernetes.
Long Description:
Please note the following:
- This is an introductory course
- An In-depth coverage of Math / Stats behind Machine Learning is beyond the scope of this course.
- Course is taught using Spark & Python (Pyspark environment).
- Working knowledge of Spark is essential for this course.
Course Code/Duration:
BDT68 / 1 Day
Learning Objectives:
After this course, you will be able to:
- Use Apache Spark for Machine Learning .
- Learn popular machine learning algorithms,
- Learn about Machine Learning Algorithm's applicability, and limitations.
- Practice the application of these algorithms using Apache Spark
- Good programming background
- Working knowledge of Spark is essential for this course
- Familiarity with Python is a plus, but not required
- No machine learning knowledge is required
- Computer science background
- This course is designed for Data Analysts and Software Engineers
- This course is designed for Data Analysts and Software Engineers
Course Outline:
Section 1: Machine Learning (ML) Overview
- Machine Learning landscape
- Machine Learning applications
- Understanding ML algorithms & models
Section 2: ML in Python and Spark
- Spark ML Overview
- Introduction to Jupyter notebooks
- Lab: Working with Jupyter + Python + Spark
- Lab: Spark ML utilities
Section 3: Machine Learning Concepts
- Covariance, Correlation, Covariance Matrix
- Errors, Residuals
- Confusion Matrix
- ROC curve, Area Under Curve (AUC)
- Lab: Basic stats
Section 4: Linear regression
- Simple Linear Regression
- Multiple Linear Regression
- Running LR
- Evaluating LR model performance
- Lab
- Use case: House price estimates
Section 5: Logistic Regression
- Understanding Logistic Regression
- Calculating Logistic Regression
- Evaluating model performance
- Lab
- Use case: credit card application
Training material provided:
Yes (Digital format)
The curriculum is empty
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