This course provides instruction on the theory and practice of data science, including machine learning and natural language processing. This course introduces many of the core concepts behind today’s most commonly used algorithms and introducing them in practical applications. We’ll discuss concepts and key algorithms in all of the major areas – Classification, Regression, Clustering, Dimensionality Reduction, including a primer on Neural Networks. We’ll focus on both single-server tools and frameworks (Python, NumPy, pandas, SciPy, Scikit-learn, NLTK, TensorFlow Jupyter) as well as large-scale tools and frameworks (Spark MLlib, Stanford CoreNLP, TensorFlowOnSpark/Horovod/MLeap, Apache Zeppelin).
Architects, software developers, analysts and data scientists who need to apply data science and machine learning on Spark/Hadoop.
Students must have experience with Python and Scala, Spark, and prior exposure to statistics, probability, and a basic understanding of big data and Hadoop principles. While brief reviews are offered in these topics, students new to Hadoop are encouraged to attend the Apache Hadoop Essentials (HDP-123) course and HDP Spark Developer (DEV-343), as well as the language-specific introduction courses.
- An Introduction to Data Science SciKit-Learn, HDFS, Reviewing Spark apps, DataFrames and NOSQL
- Algorithms in Spark ML and SciKit-Learn: Linear Regression, Logistic Regression, Support Vectors, Decision Trees
- K-Means & GMM Clustering, Essential TensorFlow, NLP with NLTK, NLP with Stanford CoreNLP
- HyperParameter Tuning, K-Fold Validation, Ensemble Methods, ML Pipelines in SparkML