MP4 | Video: h264, 1280x720 | Audio: AAC, 48 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 6 lectures (47 mins) | Size: 348 MB

This is a Hands-on 1- hour Machine Learning Project using Pyspark.

What you'll learn

Diabetes Prediction using Spark Machine Learning (Spark MLlib)

Learn Pyspark fundamentals

Working with dataframes in Pyspark

Analyzing and cleaning data

Process data using a Machine Learning model using Spark MLlib

Build and train logistic regression model

Performance evaluation and saving model

Requirements

Basics of Python

Description

You learn by Practice.

No unnecessary lectures. No unnecessary details.

A precise, to the point and efficient course about Machine learning in Spark.

About Pyspark:

Pyspark is the collaboration of Apache Spark and Python. PySpark is a tool used in Big Data Analytics.

Apache Spark is an open-source cluster-computing framework, built around speed, ease of use, and streaming analytics whereas Python is a general-purpose, high-level programming language. It provides a wide range of libraries and is majorly used for Machine Learning and Real- Streaming Analytics.

In other words, it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data. We will be using Big data tools in this project.

You will learn more in this one hour of Practice than hundreds of hours of unnecessary theoretical lectures.

Learn the most important aspect of Spark Machine learning (Spark MLlib) :

Pyspark fundamentals and implementing spark machine learning

Importing and Working with Datasets

Process data using a Machine Learning model using spark MLlib

Build and train Logistic regression model

Test and analyze the model

We will build a model to predict diabetes. This is a 1- hour project. In this hands-on project, we will complete the following tasks:

Task 1: Project overview

Task 2: Intro to Colab environment & install dependencies to run spark on Colab

Task 3: Clone & explore diabetes dataset

Task 4: Data Cleaning

Check for missing values

Replace unnecessary values

Task 5: Correlation & feature selection

Task 6: Build and train Logistic Regression Model using Spark MLlib

Task 7: Performance evaluation & Test the model

Task 8: Save & load model

Who this course is for:

Anyone interested in Data analysis with Spark and ML

Anyone who wants to learn fundamentals of Apache Spark in Big Data Analytics



DOWNLOAD
uploadgig