PySpark for Data Science - IV: Machine Learning
Dive into the world of big data processing with PySpark, the Python library for Apache Spark. Learn how to process, analyze, and derive insights from massive datasets using Python’s user-friendly interface. Elevate your data skills with PySpark. Dive deep into distributed data processing, machine learning, streaming, and more to navigate the vast oceans of big data.
- Learn how to process, analyze, and derive insights from massive datasets using Python’s user-friendly interface.
- Elevate your data skills with PySpark. Dive deep into distributed data processing, machine learning, streaming, and more to navigate the vast oceans of big data.
Prabhakaran SPrincipal Data Scientist
Included in All-Access, one membership for every course.
- Lessons
- 27, in 4 chapters
- Video
- 1 h 19 min
- Free to watch
- 1 lesson
- Certificate
- On completion
What you'll be able to do
- PySpark linear regression
- PySpark Logistic regression
- PySpark LASSO Regression
- PySpark Ridge Regression
Course content
4 chapters · 27 lessons · 1 h 19 min · 1 free to preview
01Linear Regression11 lessons · 43 min
- Why Pyspark for ML when we have scikit-learn?Preview10:45
- Download Resources1:00
- Import libraries and init SparkSession1:45
- Using VectorAssembler to prepare data8:35
- Build the linear regression model7:11
- Model Summary3:17
- Make predictions and evaluate the model1:45
- Analyze feature importance5:42
- Improve the model (optional)1:12
- Save and load the model (optional)1:03
- Course Review1:00
02Logistic Regression4 lessons · 18 min
- Setup and Load Dataset2:55
- Prepare the data5:03
- Building the Logistic Regression model7:06
- Evaluating the model on test data2:58
03Ridge Regression6 lessons · 15 min
- Setup and Load1:12
- Prepare the data1:37
- Creating a Ridge Regression model7:36
- Hyperparameter tuning2:13
- Inspect the model coefficients and intercept1:43
- Evaluating the model1:01
04LASSO Regression6 lessons · 4 min
- Import required libraries and initialize SparkSession0:50
- Prepare the data0:24
- Build Lasso Regression Model0:27
- Hyperparameter tuning1:02
- Inspect the model coefficients and intercept0:47
- Evaluating the model0:41
What you need
- Basics of Python
- Foundational knowledge of Data Science
- High school maths
Who this course is for
- Data Science Aspirants
- Data Science Professionals
- Software/Data engineers interested in quantitative analysis
- Professionals working with large datasets
- Data analysts, economists, researchers
Your instructor
Prabhakaran S
Principal Data Scientist
My name is Selva, and I am super excited to mentor you on this course!
I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.
I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!
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Questions
How do I get this course?
It is part of All-Access: one membership that opens all 85 courses. The plans and prices are on our course site. You can watch the free preview lessons before you join.
Can I get a refund?
Yes. If the membership is not right for you, ask for a refund within 30 days of your first payment.
Can I learn on my phone?
Yes. Lessons play in the browser on any phone, tablet or computer, and your place is saved for the next device.
I am already a member. Where are my courses?
On our course site, edu.machinelearningplus.com. Sign in there with the email you joined with. The courses move to this site later, with your access, and you will not pay again.
