Explore the exciting world of machine learning with this IBM course. Start by learning ML fundamentals before unlocking the power of Apache Spark to build and deploy ML models for data engineering applications. Dive into supervised and unsupervised learning techniques and discover the revolutionary possibilities of Generative AI through instructional readings and videos. Gain hands-on experience with Spark structured streaming, develop an understanding of data engineering and ML pipelines, and become proficient in evaluating ML models using SparkML. In practical labs, you'll utilize SparkML for regression, classification, and clustering, enabling you to construct prediction and classification models. Connect to Spark clusters, analyze SparkSQL datasets, perform ETL activities, and create ML models using Spark ML and sci-kit learn. Finally, demonstrate your acquired skills through a final assignment. This intermediate course is suitable for aspiring and experienced data engineers, as well as working professionals in data analysis and machine learning. Prior knowledge in Big Data, Hadoop, Spark, Python, and ETL is highly recommended for this course.
AS
Excellent learning experience - well-designed content and practical sessions.
RR
Liked the structure and labs. Good way to learn with handson experience.
SK
After completing this course. I gained much more knowledge about Spark that apply in ML project. and other topics such as data engineering, ML lifecycle.
SR
It good to learn basics and widen my knowledge in Data Engineering
SS
Spark is build to work with distributed systems. But here it looks like it is some strange twin of pandas. Some basics of Distributed Systems wanted with at least one at least theoretical examlple.
BS
This course gives good overview about ML with Spark.
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The final project grading system is bad. Even you follow all the steps accordingly, you might not be able to find the same coefficients as required.
AI narrated slides with information that covers many topics with insufficient depth. The manner in which the final project is evaluated is just... ouch.
The course is too much focused on Apache. I would have appreciated more ML theory and practical examples.
The updated course is well organized and useful for a workflow or a standard procedure of PySpark.
After completing this course. I gained much more knowledge about Spark that apply in ML project. and other topics such as data engineering, ML lifecycle.
Excellent learning experience - well-designed content and practical sessions.
As usually another IBM course is very bad. It gives a big list of things you have to memorize without explaining anything. Listening to the lecturer is very hard. She definitely needs to train story telling. The only good thing in these courses is a sillabus. That's why I continue to watch but I wish there would be a better course set on Data engineering topic.
Liked the structure and labs. Good way to learn with handson experience.
This course gives good overview about ML with Spark.
Simple, understandable, and learnable :)
The course is hands on and very relevant
Very good introduction to Spark ML
Good course for beginners.
the lab are very helpful
Wonderful, Clear course
Great Course
Lovely.
Great!
GOOD