Would you like to build, train, test and evaluate a machine learning model that is able to detect diabetes using logistic regression’this is a Hands-on Machine Learning Course where you will practice alongside the classes. The dataset will be provided to you during the lectures. We highly recommend that for the best learning experience, you practice alongside the lectures. 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 learningImporting and Working with DatasetsProcess data using a Machine Learning model using spark MLlibBuild and train Logistic regression modelTest and analyze the modelThe entire course has been divided into tasks. Each task has been very carefully created and designed to give you the best learning experience. In this hands-on project, we will complete the following tasks: Task 1: Project overviewTask 2: Intro to Colab environment & install dependencies to run spark on ColabTask 3: Clone & explore the diabetes datasetTask 4: Data CleaningTask 5: Correlation & feature selectionTask 6: Build and train Logistic Regression Model using Spark MLlibTask 7: Performance evaluation & Test the modelTask 8: Save & load modelAbout 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-Time 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. Make a leap into Data science with this Spark MLlib project and showcase your skills on your resume. Click on the ENROLL NOW button and start learning. Happy Learning.
