Apache Spark, a significant component in the Hadoop Ecosystem, is a cluster computing engine used in Big Data. Building on top of the Hadoop YARN and HDFS ecosystem, offers order-of-magnitude faster processing for many in-memory computing tasks compared to Map/Reduce. It can be programmed in Java, Scala, Python, and R - the favorite languages of Data Scientists - along with SQL-based front ends.
The first part of the course teaches performing Machine Learning at Scale using the popular Apache Spark framework. This course is intended for data scientists and software engineers, and assumes attendees have little or no previous experience with Machine Learning. This course explores popular machine learning algorithms from the ground up. Students will explore Apache Spark essentials, core machine learning concepts, regressions, classifications, clustering and more.
The abundance of data and affordable cloud scale has led to an explosion of interest in Deep Learning. Google has released an excellent library called TensorFlow to open-source, allowing state-of-the-art machine learning done at scale, complete with GPU-based acceleration. Students will explore these skills in an active hands-on manner. The second part of the course introduces students to Deep Learning concepts and how TensorFlow implements them.
This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Throughout the program, working in a hands-on learning environment guided by our expert instructor, students will
Need different skills or topics? If your team requires different topics or tools, additional skills or custom approach, this course may be easily adjusted to accommodate. We offer additional related Machine Learning, AI, Deep Learning, data science, programming (Python, R, Java, Scala etc.) and development courses which may be blended with this course for a track that best suits your learning objectives. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.
This is an intermediate level course, geared for Data Scientists, Data Analysts and Developers new to Machine Learning, Spark and TensorFlow.
Pre-Requisites: Students should have attended or have incoming skills equivalent to those in this course:
Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.
Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience needs and skill-level.
Part 1: Introduction to Machine Learning
Machine Learning (ML) Overview
ML in Python and Spark
Machine Learning Concepts
Feature Engineering (FE)
Linear Regression
Logistic Regression
Classification: SVM (Supervised Vector Machines)
Classification: Decision Trees & Random Forests
Classification: Naive Bayes
Clustering (K-Means)
Principal Component Analysis (PCA)
Recommendations (Collaborative filtering)
Performance
Part Two: Introduction to Deep Learning with TensorFlow
Introducing TensorFlow
The Tensor: The Basic Unit of TensorFlow
Single Layer Linear Perceptron Classifier with TensorFlow
Hidden Layers: Intro to Deep Learning
High level TensorFlow: tf.learn
Convolutional Neural Networks in TensorFlow
Introducing Keras
Recurrent Neural Networks in TensorFlow
Long Short-Term Memory (LSTM) in TensorFlow
Conclusion
Each student will receive a Student Guide with course notes, code samples, setp-by-step written lab instructions, software tutorials, diagrams and related reference materials and links (as applicable). Students will also receive related (as applicable) project files, code files, data sets and solutions required for any hands-on work.
Lab Setup Made Simple. All course labs and solutions, data sets, software, detailed courseware, lab guides and resources (as applicable) are provided for attendees in our easy access, no installation required, remote lab environment. Our tech team will help set up, test and verify lab access for each attendee prior to the course start date, ensuring a smooth start to class and successful hands-on course experience for all participants.
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