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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.

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Unlimited Duration

Last Updated

July 29, 2021

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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.

Course Curriculum

    • First Steps to Scalability 00:00:00
    • Explaining scalability in detail 00:00:00
    • Python for large scale machine learning 00:00:00
    • Python packages 00:00:00
    • Scalable Learning in Scikit-learn 00:00:00
    • Out-of-core learning 00:00:00
    • Streaming data from sources 00:00:00
    • Stochastic learning 00:00:00
    • Feature management with data streams 00:00:00
    • Fast SVM Implementations 00:00:00
    • Datasets to experiment with on your own 00:00:00
    • Support Vector Machines 00:00:00
    • Feature selection by regularization 00:00:00
    • Including non-linearity in SGD 00:00:00
    • Hyperparameter tuning 00:00:00
    • Neural Networks and Deep Learning 00:00:00
    • The neural network architecture 00:00:00
    • Neural networks and regularization 00:00:00
    • Neural networks and hyperparameter optimization 00:00:00
    • Neural networks and decision boundaries 00:00:00
    • Deep learning at scale with H2O 00:00:00
    • Deep learning and unsupervised pretraining 00:00:00
    • Deep learning with theanets 00:00:00
    • Autoencoders and unsupervised learning 00:00:00
    • Deep Learning with TensorFlow 00:00:00
    • TensorFlow installation 00:00:00
    • Machine learning on TensorFlow with SkFlow 00:00:00
    • Keras and TensorFlow installation 00:00:00
    • Convolutional Neural Networks in TensorFlow through Keras 00:00:00
    • CNN’s with an incremental approach 00:00:00
    • GPU Computing 00:00:00
    • Classification and Regression Trees at Scale 00:00:00
    • Bootstrap aggregation 00:00:00
    • Random forest and extremely randomized forest 00:00:00
    • Fast parameter optimization with randomized search 00:00:00
    • CART and boosting 00:00:00
    • XGBoost 00:00:00
    • Out-of-core CART with H2O 00:00:00
    • Unsupervised Learning at Scale 00:00:00
    • Unsupervised methods 00:00:00
    • Feature decomposition – PCA 00:00:00
    • PCA with H2O 00:00:00
    • Clustering – K-means 00:00:00
    • K-means with H2O 00:00:00
    • LDA 00:00:00
    • Distributed Environments – Hadoop and Spark 00:00:00
    • From a standalone machine to a bunch of nodes 00:00:00
    • Setting up the VM 00:00:00
    • The Hadoop ecosystem 00:00:00
    • Spark 00:00:00
    • Practical Machine Learning with Spark 00:00:00
    • Setting up the VM for this chapter 00:00:00
    • Sharing variables across cluster nodes 00:00:00
    • Data preprocessing in Spark 00:00:00
    • Machine learning with Spark 00:00:00
    • Summary 00:00:00

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