Machine Learning with PySpark

本课程是 PySpark for Data Science 专项课程的一部分

位教师：Edureka

访问权限由 Coursera Learning Team 提供

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Implement machine learning models using PySpark MLlib.
Implement linear and logistic regression models for predictive analysis.
Apply clustering methods to group unlabeled data using algorithms like K-means.
Explore real-world applications of PySpark MLlib through practical examples.

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作业

14 项作业

授课语言：英语（English）

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本课程是 PySpark for Data Science 专项课程专项课程的一部分

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该课程共有4个模块

Machine Learning with PySpark introduces the power of distributed computing for machine learning, equipping learners with the skills to build scalable machine learning models. Through hands-on projects, you will learn how to use PySpark for data processing, model building, and evaluating machine learning algorithms.

By the end of this course, you will be able to: - Understand the fundamentals of PySpark and its architecture - Load, process, and manipulate large-scale datasets using PySpark’s DataFrame and RDD APIs Build machine learning models with PySpark’s MLlib, covering classification, regression, and clustering techniques - Optimize and tune machine learning models for better performance - Apply techniques for feature engineering, model evaluation, and hyperparameter tuning in a distributed environment Who Should take this Course: This course is ideal for data professionals, aspiring data engineers, and machine learning enthusiasts who want to use PySpark to handle large-scale data and build machine learning models. Prerequisites: Some prior knowledge of Python and machine learning concepts is recommended. Join us to enhance your data processing and machine learning skills with PySpark and take your expertise to the next level!

This module will instruct you on setting up of an environment for the implementation of machine learning algorithms using PySpark MLlib. You will gain a fundamental understanding of the importance of machine learning in the context of big data and explore the implementation of machine learning models using PySpark.

涵盖的内容

27个视频5篇阅读材料4个作业3个讨论话题

27个视频总计138分钟

Course Introduction: Machine Learning with PySpark 2分钟
BigData and Distributed Systems 7分钟
Introduction to MLlib 4分钟
Key Features and Applications of MLlib 4分钟
What is Machine Learning 6分钟
Types of Machine Learning 7分钟
Applications of Machine Learning 6分钟
Why PySpark for ML 7分钟
Supervised Machine Learning 3分钟
Applications of Supervised Machine Learning 4分钟
Unsupervised Machine Learning 7分钟
Significance of Unsupervised Machine Learning 2分钟
Semi-supervised Machine Learning 5分钟
Significance of Semi-supervised Machine Learning 4分钟
Machine Learning Pipelines 5分钟
Benefits of ML Pipeline 4分钟
Linear Regression 5分钟
Linear Regression Use case 5分钟
Implementing Linear Regression 7分钟
Data Ingestion 6分钟
Logistic Regression 5分钟
Applications of Logistic Regression 3分钟
Key Concepts of Logistic Regression 3分钟
Logistic Regression Use case 6分钟
Evaluating Logistic Regression 7分钟
Decision Trees 7分钟
Decision Trees Use case 6分钟

5篇阅读材料总计50分钟

Welcome to Machine Learning with PySpark 10分钟
Introduction to Machine Learning with PySpark 10分钟
Machine Learning Algorithms 10分钟
Importance of Machine Learning with PySpark 10分钟
Module Summary: Introduction to PySpark Machine Learning 10分钟

4个作业总计38分钟

Practice Quiz: Overview of PySpark MLlib 6分钟
Practice Quiz: Introduction to Machine Learning 6分钟
Practice Quiz: Implementing Machine Learning Algorithms 6分钟
Knowledge Check: Introduction to PySpark MLlib 20分钟

3个讨论话题总计30分钟

Introduce Yourself 10分钟
Working with PySpark 10分钟
Which Machine Learning algorithm do you think is widely used? 10分钟

In this module, you will be able to explore the foundations of unsupervised machine learning, focusing on techniques for analyzing unlabeled data. You will dive into clustering algorithms like K-means, learning how to group data points based on similarities. Additionally, you will discover the power of Association Rule Mining, uncovering hidden patterns and relationships in datasets without predefined labels.

涵盖的内容

26个视频6篇阅读材料5个作业1个讨论话题

26个视频总计134分钟

What is Unsupervised Learning 6分钟
Applications of Unsupervised Learning 3分钟
How Algorithms differ between Supervised and Unsupervised 5分钟
Unsupervised Machine Learning Algorithms 5分钟
What is Distance metrics 4分钟
Significance of Distance metrics 3分钟
Types of Distance metrics 7分钟
Labeling techniques 5分钟
Significance of Data Labeling 5分钟
One hot encoding 7分钟
What is Clustering 6分钟
Types of Clustering 4分钟
K-means Clustering 6分钟
Applications of K-means Clustering 4分钟
Elbow method 5分钟
Silhouette Method 4分钟
Demonstration of K-means Clustering 5分钟
Model Evaluation for K-means Clustering 5分钟
What is Association Rule Mining 7分钟
Implementing of FP Growth 4分钟
Basic concepts of Association Rule Mining 5分钟
Lift and Frequent Itemsets 4分钟
Applications of Association Rule Mining 5分钟
Use cases of Association Rule Mining 3分钟
Demonstration of Association Rule Mining 7分钟
Association Rule Mining using FP Growth 7分钟

6篇阅读材料总计65分钟

Unsupervised Machine Learning with PySpark 10分钟
Explore DBSCAN 15分钟
Dimensionality Reduction 10分钟
Gaussian Mixture Models (GMM) 10分钟
FP-Growth Algorithm in PySpark 10分钟
Module Summary: Advanced PySpark Machine Learning 10分钟

5个作业总计44分钟

Practice Quiz: Unsupervised Machine Learning 6分钟
Practice Quiz: Data Labeling 6分钟
Practice Quiz: Overview of Clustering 6分钟
Practice Quiz: Overview of Association Rule Mining 6分钟
Knowledge Check: Overview of Unsupervised Learning 20分钟

1个讨论话题总计10分钟

Have you ever applied dimensionality reduction techniques to high-dimensional data? 10分钟

The course will equip you with the skills to evaluate machine learning models using various performance metrics and techniques in PySpark MLlib. You will also explore the future scope and potential applications of MLlib in real-world scenarios, gaining insights into how it can be applied to different industries and problem domains. Through case studies, you will analyze practical examples of machine learning implementations.

涵盖的内容

18个视频2篇阅读材料4个作业2个讨论话题

18个视频总计99分钟

Evaluating ML Models 2分钟
Steps for Evaluating Models 7分钟
RMSE 7分钟
R-squared 6分钟
Significance of R-Squared 2分钟
Saving and Loading Models 7分钟
Future scope of MLlib 7分钟
Applications of Social Media 6分钟
Applications of Entertainment 7分钟
Applications of Business 6分钟
Applications of Finance 5分钟
Customer churn prediction 5分钟
Model Training for Customer Churn Prediction 7分钟
Model Evaluation for Customer Churn Prediction 5分钟
Market Basket Analysis 4分钟
FP Growth for Market Basket Analysis 6分钟
Predictive Maintenance 4分钟
Model Evaluation in Predictive Maintainance 5分钟

2篇阅读材料总计20分钟

Model Evaluation Techniques in Machine Learning 10分钟
Module Summary: Applications and Case-Studies 10分钟

4个作业总计38分钟

Practice Quiz: Machine Learning Model Evaluation 6分钟
Practice Quiz: Future developments and Applications of MLlib 6分钟
Practice Quiz: Case-studies of MLlib 6分钟
Knowledge Check: Applications and Case studies of MLlib 20分钟

2个讨论话题总计20分钟

Have you had experience applying machine learning in various industries? 10分钟
In what ways do you think MLlib is utilized in the Health and Medical sector? 10分钟

This module is meant to test how well you understand the different ideas and lessons you've learned in this course. You will undertake a project based on these PySpark concepts and complete a comprehensive quiz that will assess your confidence and proficiency in Machine Learning with PySpark.