Angie Ma offers a condensed introduction to key data science and machine learning concepts and techniques, showing you what is (and isn't) possible with these exciting new tools and how they can benefit your organization.
Machine learning with TensorFlow
Dana Mastropole (The Data Incubator)
The TensorFlow library provides for the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms. This training will introduce TensorFlow's capabilities through its Python interface.
Data engineering and architecture, Streaming systems and real-time applications
Real-time systems with Spark Streaming and Kafka
Jesse Anderson (Big Data Institute)
To handle real-time big data, you need to solve two difficult problems: how do you ingest that much data and how will you process that much data? Jesse Anderson explores the latest real-time frameworks (both open source and managed cloud services), discusses the leading cloud providers, and explains how to choose the right one for your company.
Data science and machine learning
Apache Spark programming
The instructor walks you through the core APIs for using Spark, fundamental mechanisms and basic internals of the framework, SQL and other high-level data access tools, and Spark’s streaming capabilities and machine learning APIs.
Data science and machine learning with Apache Spark
behzad bordbar (Cloudera)
Behzad Bordbar demonstrates how to implement typical data science workflows using Apache Spark. You'll learn how to wrangle and explore data using Spark SQL DataFrames and how to build, evaluate, and tune machine learning models using Spark MLlib.
Hands-on data science with Python
Zachary Glassman (The Data Incubator)
The Data Incubator offers a foundation in building intelligent business applications using machine learning. We will walk through all the steps - from prototyping to production - of developing a machine learning pipeline. We’ll look at data cleaning, feature engineering, model building/evaluation, and deployment. Students will extend these models into two applications from real-world datasets.
Machine learning with PyTorch
Delip Rao (R7 Speech Science)
PyTorch is a recent deep learning framework from Facebook that is gaining massive momentum in the deep learning community. Its fundamentally flexible design makes building and debugging models straightforward, simple, and fun. Delip Rao walks you through PyTorch's capabilities and demonstrates how to use PyTorch to build deep learning models and apply them to real-world problems.