ML with Python and Spark Training Overview
This Applied Machine Learning using Python and Apache Spark training teaches attendees Machine Learning (ML) concepts, terminology, and usage. Students learn how to perform and scale ML tasks using Python libraries (including NumPy, Pandas, Matplotlib, and Scikit-learn) on the Apache Spark platform.
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- Gain a basic understanding of Machine Learning
- Understand the differences between supervised and unsupervised learning
- Understand how to use Python libraries to explore, clean, and prepare data
- Describe the role of ML and where it fits into IT strategies
- Explain the technical and business drivers that result from using Machine Learning
- Understand techniques like classification, clustering, and regression
- Discuss how to identify which techniques should be applied for a specific use case
- Understand popular machine offerings, including Amazon Machine Learning, TensorFlow, Azure Machine Learning, Google Cloud, Spark MLlib, Python, R, and more
- Install and set up Anaconda
- Use Jupyter Notebooks
- Understand the popular Machine Learning algorithms, including linear regression, decision tree, logistic regression, K-nearest neighbor, K-means clustering, and more
- Use Python libraries like NumPy, Pandas, Matplotlib and Scikit-learn
- Understand Apache Spark Processing Framework and distributed architecture
- Compare Machine learning using Python versus Apache Spark
- Use Databricks cloud with Apache Spark MLlib
Prerequisites
All attendees must have familiarity with Python. Having a working knowledge of Spark is a plus, but not required.
Outline
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Introduction
- History and background of Machine Learning
- Compare traditional programming to Machine Learning
- Supervised and unsupervised learning overview
Machine Learning Patterns
- Classification
- Clustering
- Regression
Gartner Hype Cycle for Emerging Technologies
- Machine Learning offerings in the industry
- Install and set up Anaconda
- Descriptive statistics
- Jupyter Notebooks
Exploratory Data Analysis
Getting Data
Feature Selection
Transforming Data
Binary Encoding
One-Hot Encoding
Feature Engineering
Algorithms
- Linear regression
- Naive Bayes
- Decision tree
- Random forest
- Logistics regression
- Support vector machine
- K-nearest neighbor
- K-means clustering
Data Modeling
Machine Learning using Python Versus using Spark
Databricks Cloud Community Account Setup
Measuring Performance
- Confusion Matrix
- ROC Curve, Area Under Curve (AUC)
Refining the Model
Hyperparameter Tuning
Grid Search
Spark MLlib
Conclusion and Next Steps
Training Materials
All Machine Learning training students receive comprehensive courseware.
Software Requirements
- Windows, Mac, or Linux with at least 8 GB RAM
- Most class activities will create Spark code and visualizations in a browser-based notebook environment. The class also details how to export these notebooks and how to run code outside of this environment.
- A current version of Anaconda for Python 3.x
- Related lab files that Accelebrate will provide
- Internet access
