PySpark Essentials for Data Scientists (Big Data + Python)
Learn how to wrangle Big Data for Machine Learning using Python in PySpark taught by an industry expert!
4.44 (822 reviews)
5 657
students
17.5 hours
content
May 2022
last update
$54.99
regular price
What you will learn
Use Python with Big Data on a distributed framework (Apache Spark)
Work with REAL datasets on realistic consulting projects
How to streaming LIVE data from Twitter using Spark Structured Streaming
Learn how to create a "Pandora Like" app that classifies songs into genres using machine learning
Flag suspicious job postings using Natural Language Processing
Use machine learning to predict optimal cement strength and the factors that affect it
Classify Christmas cooking recipes using Topic Modeling (LDA)
Customer Segmentation using Gaussian Mixture Modeling (Clustering)
Use cluster analysis to develop a strategy designed to increase college graduation rates for under-priveleged populations
How to use the k-means clustering algorithm to define a marketing outreach strategy
Integrate a UI to monitor your model training and development process with MLflow
Theory and application of cutting edge data science algorithms
Manipulate, Join and Aggregate Dataframes in Spark with Python
Learn how to apply Spark's machine learning techniques on distributed Dataframes
Cross Validation & Hyperparameter Tuning
Frequent Pattern Mining Techniques
Classification & Regression Techniques
Data Wrangling for Natural Language Processing
How to write SQL Queries in Spark
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Comidoc Review
Our Verdict
This course offers comprehensive coverage of PySpark, particularly within the context of machine learning applications. However, improvements can be made regarding instructor interaction, lecture quality throughout the entire course duration, and a more consistent alignment of presented content with corresponding coding examples.
What We Liked
- Exhaustive coverage of PySpark for machine learning, from basics to advanced concepts
- Well-designed projects that provide practical experience
- Helpful custom functions provided for further use
- Instructor's elegant and graceful approach to programming
Potential Drawbacks
- Insufficient responses from the instructor, leading to unresolved questions
- Rapid decline in lecture quality during machine learning portion
- Lack of detailed explanations for typos and unintuitive syntax
- Occasional mismatch between presented content and coding content
Related Topics
2839728
udemy ID
27/02/2020
course created date
04/10/2020
course indexed date
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