Course Description

Machine Learning (ML) and Deep Learning (DL) are sub-domains under the umbrella known as Artificial Intelligence (AI). Machine learning refers to extracting patterns that cause change in predicates and then infer the predicate based on data prepared from experience. Machine learning is a predictive analysis technique that can facilitate many business decisions in a wide range of work domains viz. Healthcare, Finance, Production, Automobile. These ML models are “trained” for the specific problem by means of training data drawn from the problem space. Deep learning is the subset of Machine Learning which is a neural network which attempts to simulate the behavior of the human brain allowing it to learn from large amount of data. Deep learning drives many AI applications and services that improve automation, performing analytical and physical tasks without human intervention.

Advancements in ML, Cloud, and ability to leverage high performance or accelerated compute power changed the way we are handling data and prepare it to train model. Spark MLLib offers these capabilities. Machine Learning with Apache Spark examines various technologies for building end-to-end distributed machine learning platforms based on the Apache Spark ecosystem with Spark MLlib, TensorFlow, Horovod, PyTorch, and more.

This course primarily focuses on various stages of Machine Learning Life cycle, Deep Neural Networks, Deep Distributed Neural Networks and how we can use Apache Spark ecosystem and Spark MLlib to build and train machine learning and deep learning models.

After completing this course, you will be able to

  • Understand the open-source tools used for ML and DL
  • Demystify Machine and Deep Learning Techniques
  • Explore and Understand the Apache Spark ecosystem for ML and DL
  • Prepare and Transform data for model Training
  • Implement the phases of ML Life Cycle
  • Train and Evaluate Deep Learning and Distributed Deep Learning Models
  • Design and implement ML applications
  • Do intelligent conversations with Data Scientists, AI/ML developers
  • Open-up new opportunities for HPE

Prerequisites

Course Outline

In this module, you will explore and learn about Data preparation tasks - Data preprocessing /cleaning, Feature Engineering and how pyspark built-in capabilities can be used to accomplish these tasks.

In this module, you will learn about ML Types and Models along with evaluation parameters and machine learning life cycle stages.

In this module, you will learn about Neural networks, a beautiful biologically inspired programming paradigm which enables a computer to learn from observational data and Deep learning, a powerful set of techniques for learning in neural networks.

In this module, you will understand distributed deep learning and how training time is massively reduced by distributing training tasks over multiple CPUs and leveraging Spark capability for same.

Reach Out to Us for Any Details/Enrolment

deepanshib@cloudthat.com