Scalable Machine Learning
Implement the Scalable Machine Learning using the Hadoop and Spark framework in either Scala or Python language
Duration
3 Days
Level
Intermediate Level
Design and Tailor this course
As per your team needs
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This course has been designed and developed for providing exposure to participants in Scalable Machine learning. Cloudera Hadoop and Spark Frameworks being used for implementing Scalable Machine Learning Algorithms using Scala/Python programming language.
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This program is designed for:
- Software Developer
- Data Scientist
- Data Engineer
- Big Data Engineer
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- What is Scalable Machine Learning?
- Why it is required?
- Key platforms for performing Scalable Machine Learning
- Scalable Machine Learning Project End to End Pipeline
- Spark Introduction
- Why Spark for Scalable Machine Learning?
- Databricks Platform Demo
- Approaches for scaling sci-kit learn code
- Hands-on Exercise(s): Experiencing the first notebook
- Problems with Traditional Machine Learning Frameworks
- Machine Learning at Scale – Various options
- Iterative Algorithms
- How Spark performs well for Iterative Machine Learning Algorithms?
- Hands-on Exercise(s)
- Acquiring Structured content from Relational Databases
- Acquiring Semi-structured content from Log Files
- Acquiring Unstructured content from other key sources like Web
- Tools for Performing Data acquisition at Scale
- Sqoop, Flume and Kafka Introduction, use cases and architectures
- Hands-on Exercise(s)
- Using the Spark Shell
- Resilient Distributed Datasets (RDDs)
- Functional Programming with Spark
- RDD Operations
- Key-Value Pair RDDs
- MapReduce and Pair RDD Operations
- Building and Running a Spark Application
- Performing Data Validation
- Data De-Duplication
- Detecting Outliers
- Hands-on Exercise(s)
- Dealing with RDD Infinite Lineages
- Caching Overview
- Distributed Persistence
- Checkpointing of an Iterative Machine Learning Algorithm
- Hands-on Exercise(s)
- Introduction
- Dataframe API
- Performing ad-hoc query analysis using Spark SQL
- Hands-on Exercise(s)
- Spark ML vs Spark MLLib
- Data types and key terms
- Feature Extraction
- Linear Regression using Spark MLLib
- Hands-on Exercise(s)
- Spark ML Overview
- Transformers and Estimators
- Pipelines
- Implementing Decision Trees
- K-Means Clustering using Spark ML
- Hands-on Exercise(s)
- Types – Classification and Regression trees
- Gini Index, Entropy and Information Gain
- Building Decision Trees
- Pruning the trees
- Prediction using Trees
- Ensemble Models
- Bagging and Boosting
- Advantages of using Random Forest
- Working with Random Forest
- Ensemble Learning
- How ensemble learning works
- Building models using Bagging
- Random Forest algorithm
- Random Forest model building
- Fine tuning hyper-parameters
- Hands-on Exercise(s)
- Model Evaluation
- Optimizing a Model
- Deploying Model
- Best Practices
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Participants should have knowledge level equivalent to what is specified in “Data and Machine Learning Fundamentals” course (Beginner level course in “Machine Learning and Artificial Intelligence” learning path)
