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Large Scale Machine Learning with Python

  • Course Code: Data Science - Large Scale Machine Learning with Python
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for those who wants to Learn to build powerful machine learning models quickly and deploy large-scale predictive applications

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level R Large Scale Machine Learning with Python for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Learn to build powerful machine learning models quickly and deploy large-scale predictive applications 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. 

Working in a hands-on learning environment, led by our Machine Learning with Python expert instructor, students will learn about and explore: 

  • Design, engineer and deploy scalable machine learning solutions with the power of Python 
  • Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework 
  • Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Apply the most scalable machine learning algorithms 
  • Work with modern state-of-the-art large-scale machine learning techniques 
  • Increase predictive accuracy with deep learning and scalable data-handling techniques 
  • Improve your work by combining the MapReduce framework with Spark 
  • Build powerful ensembles at scale 
  • Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine 

Audience & Pre-Requisites 

This course is designed for developers wants to Learn to build powerful machine learning models quickly and deploy large-scale predictive applications 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. First Steps to Scalability 
  • First Steps to Scalability 
  • Explaining scalability in detail 
  • Python for large scale machine learning 
  • Python packages 
  1. Scalable Learning in Scikit-learn 
  • Scalable Learning in Scikit-learn 
  • Out-of-core learning 
  • Streaming data from sources 
  • Stochastic learning 
  • Feature management with data streams 
  1. Fast SVM Implementations 
  • Fast SVM Implementations 
  • Datasets to experiment with on your own 
  • Support Vector Machines 
  • Feature selection by regularization 
  • Including non-linearity in SGD 
  • Hyperparameter tuning 
  1. Neural Networks and Deep Learning 
  • Neural Networks and Deep Learning 
  • The neural network architecture 
  • Neural networks and regularization 
  • Neural networks and hyperparameter optimization 
  • Neural networks and decision boundaries 
  • Deep learning at scale with H2O 
  • Deep learning and unsupervised pretraining 
  • Deep learning with theanets 
  • Autoencoders and unsupervised learning 
  1. Deep Learning with TensorFlow 
  • Deep Learning with TensorFlow 
  • TensorFlow installation 
  • Machine learning on TensorFlow with SkFlow 
  • Keras and TensorFlow installation 
  • Convolutional Neural Networks in TensorFlow through Keras 
  • CNN’s with an incremental approach 
  • GPU Computing 
  1. Classification and Regression Trees at Scale 
  • Classification and Regression Trees at Scale 
  • Bootstrap aggregation 
  • Random forest and extremely randomized forest 
  • Fast parameter optimization with randomized search 
  • CART and boosting 
  • XGBoost 
  • Out-of-core CART with H2O 
  1. Unsupervised Learning at Scale 
  • Unsupervised Learning at Scale 
  • Unsupervised methods 
  • Feature decomposition – PCA 
  • PCA with H2O 
  • Clustering – K-means 
  • K-means with H2O 
  • LDA 
  1. Distributed Environments – Hadoop and Spark 
  • Distributed Environments – Hadoop and Spark 
  • From a standalone machine to a bunch of nodes 
  • Setting up the VM 
  • The Hadoop ecosystem 
  • Spark 
  1. Practical Machine Learning with Spark 
  • Practical Machine Learning with Spark 
  • Setting up the VM for this chapter 
  • Sharing variables across cluster nodes 
  • Data preprocessing in Spark 
  • Machine learning with Spark 
  • Summary 

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