Large Scale Machine Learning with Spark Training


Data processing, related algorithms, tuning and scaling up all in one. Spark can handle large-scale streaming data to determine when to cache data in memory and process them up to 100 times quicker than Hadoop-based MapReduce.

From this, you will learn using ML with Spark and it’s all new features; explore important advanced future engineering concepts; and know to use external libraries with Spark.

Overall, you will be able to develop complete and personalized ML applications from data collections, model building, tuning, and scaling up to deploying on a cluster or the cloud.


Large Scale Machine Learning with Spark Training



  • Understand ML algorithms and scale up ML applications
  • Develop applications using Scala, Java, R, and Python
  • Develop ML applications by handling large text and using spark streaming
  • Tune and enhance ML models



1: Introduction to Data Analytics with Spark

2: Machine Learning Best Practices

3: Understanding the Problem by Understanding the Data

4: Extracting Knowledge through Feature Engineering

5: Supervised and Unsupervised Learning by Examples

6: Building Scalable Machine Learning Pipelines

7: Tuning Machine Learning Models

8: Adapting Your Machine Learning Models

9: Advanced Machine Learning with Streaming and Graph Data

10: Configuring and Working with External Libraries



Large Scale Machine Learning with Spark Training

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Call –: +91 97899 68765 / +91 9962774619 / 044 – 42645495

Weekdays / Fast Track / Weekends / Corporate Training modes available

Large Scale Machine Learning with Spark Training Also available across India in Bangalore, Pune, Hyderabad, Mumbai, Kolkata, Ahmedabad, Delhi, Gurgon, Noida, Kochin, Tirvandram, Goa, Vizag, Mysore,Coimbatore, Madurai, Trichy, Guwahati


On-Demand Fast track Large Scale Machine Learning with Spark Training globally available also at Singapore, Dubai, Malaysia, London, San Jose, Beijing, Shenzhen, Shanghai, Ho Chi Minh City, Boston, Wuhan, San Francisco, Chongqing.