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Scalable Machine Learning and Deep Learning

  /    /  Scalable Machine Learning and Deep Learning

Scalable Machine Learning and Deep Learning

Artificial Intelligence

Scalable machine learning is a major buzzword in the machine learning industry, partly because getting machine learning processes to scale is an important and challenging aspect of many machine learning projects.

This course is intermediate level in “Machine Learning and Artificial Intelligence” learning path. It has been designed and developed for providing exposure to participants in Scalable Machine learning.  This course covers Spark Core, Spark SQL, Spark Streaming and Spark ML in detail along with providing exposure to Deep Learning in a gentle manner.

    • Understand the role of Spark in Machine Learning
    • Providing hands-on experience in Data Acquisition, Processing, Analysis and Modeling using Cloudera distribution of Hadoop and Spark
    • The participants will deal with various common types of data e.g. CSV, XML, JSON, Social Media data etc. for pre-processing and/or building Machine Learning Models using Spark
    • How Deep Cognition helps in performing Deep Learning
    • During the course, the participants will also get exposure to Deep Learning using Deep Cognition Studio
    • Build Deep Learning Models using Deep Cognition Studio even without knowledge of Statistics

This program is designed for those who aspire for Data/ML/AI roles:

  • Data Engineers
  • Data Scientists
  • Machine Learning Engineers
  • Data Integration Engineers
  • Data Architects
Understanding the Big Picture
  • Artificial Intelligence (AI) Overview
  • AI vs ML vs Data Science
  • The relationship between Deep Learning (DL) and Machine Learning
  • Practical Use cases
  • Concepts and Terms
  • Tools/Platforms for Scalable ML, DL, and AI
  • Big Data and Cloud fits into the Ecosystem
Introduction to Scalable Machine Learning
  • What is Scalable Machine Learning?
  • Why it is required?
  • Key platforms for performing Scalable Machine Learning
  • Scalable Machine Learning Project End to End Pipeline
  • Spark Introduction
  • Why Spark for Scalable Machine Learning?
  • Databricks Platform Demo
  • Approaches for scaling sci-kit learn code
  • Hands-on Exercise(s): Experiencing the first notebook
Why Spark for Scalable Machine Learning (SML)?
  • Problems with Traditional Machine Learning Frameworks
  • Machine Learning at Scale – Various options
  • Iterative Algorithms
  • How Spark performs well for Iterative Machine Learning Algorithms?
  • Hands-on Exercise(s)
Scalable Machine Learning on Enterprise Platform
  • Quick Recap/Introduction to Hadoop  
  • Logical View of Cloudera Distribution
  • Big Data Analytics Pipelines
  • Components in Cloudera Distribution for performing SML
  • Hands-on Exercise(s)
Data Acquisition at Scale
  • Acquiring Structured content from Relational Databases
  • Acquiring Semi-structured content from Log Files
  • Acquiring Unstructured content from other key sources like Web
  • Tools for Performing Data acquisition at Scale
  • Sqoop, Flume and Kafka Introduction, use cases and architectures
  • Hands-on Exercise(s)
Data Pre-Processing for Modeling
  • Using the Spark Shell
  • Resilient Distributed Datasets (RDDs)
  • Functional Programming with Spark
  • RDD Operations
  • Key-Value Pair RDDs
  • MapReduce and Pair RDD Operations
  • Building and Running a Spark Application
  • Performing Data Validation
  • Data De-Duplication
  • Detecting Outliers
  • Hands-on Exercise(s)
Working with Iterative Algorithms
  • Dealing with RDD Infinite Lineages
  • Caching Overview
  • Distributed Persistence
  • Checkpointing of an Iterative Machine Learning Algorithm
  • Hands-on Exercise(s)
Spark SQL
  • Introduction
  • Dataframe API
  • Performing ad-hoc query analysis using Spark SQL
  • Hands-on Exercise(s)
Spark Machine Learning using MLLib
  • Spark ML vs Spark MLLib
  • Data types and key terms
  • Feature Extraction
  • Linear Regression using Spark MLLib
  • Hands-on Exercise(s)
Spark Machine Learning using ML
  • Spark ML Overview
  • Transformers and Estimators
  • Pipelines
  • Implementing Decision Trees
  • K-Means Clustering using Spark ML
  • Hands-on Exercise(s)
Natural Language Processing
  • What is Natural Language Processing?
  • The NLTK package
  • Preparing text for analysis
  • Text summarisation
  • Sentiment analysis
  • Naïve Bayes technique
  • Text classification
  • Topic Modelling
  • Hands-on Exercise(s)
Model Evaluation, Optimization and Deployment
  • Model Evaluation
  • Optimizing a Model
  • Deploying Model
  • Best Practices
Decision Trees and Random Forest
  • Types – Classification and Regression trees
  • Gini Index, Entropy and Information Gain
  • Building Decision Trees
  • Pruning the trees
  • Prediction using Trees
  • Ensemble Models
  • Bagging and Boosting
  • Advantages of using Random Forest
  • Working with Random Forest
  • Ensemble Learning
  • How ensemble learning works
  • Building models using Bagging
  • Random Forest algorithm
  • Random Forest model building
  • Fine tuning hyper-parameters
  • Hands-on Exercise(s)
Real-time Analytics
  • Real-time data acquisition using Kafka
  • Salient Features of Kafka
  • Kafka Use cases
  • Comparing Kafka with other Key tools
  • End to End Data Pipeline using Kafka
  • Integrating Kafka with Spark Streaming
  • Hands-on Exercise(s)
Introduction to Deep Learning
  • What is Deep Learning?
  • Deep Learning Architecture
  • Deep Learning Frameworks
  • The relationship between Deep Learning and Machine Learning
  • Deep Learning Use cases
  • Concepts and Terms
  • How to implement Deep Learning?
Working with Deep Cognition Studio (DCS)
  • Deep Cognition Introduction
  • Why Deep Cognition Studio?
  • Walkthrough of Deep Learning Studio
  • Multilayer Perceptron in Deep Cognition
  • How does a single artificial neuron work?
  • Computation Graph
  • Activation Functions
  • Importance of non-linear activation
  • Data encoding for deep neural networks
  • Hands-on Exercise(s)
Building Convolution Neural Network in DCS
  • Convolutional Neural Networks
  • Components of CNN
  • Data augmentation
  • Transfer learning for using pre-trained networks
  • Hands-on Exercise(s)

Participants should have knowledge level equivalent to what is specified in “Data and Machine Learning Fundamentals” course (Beginner level course in “Machine Learning and Artificial Intelligence” learning path).

Course Information


5 Days

Mode of Delivery

Instructor led/Virtual


Beginner to Intermediate

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