Data Science & Machine Learning Training

Live Online & Classroom Enterprise Training

Become a Data Scientist and Machine Learning expert, the difference is much more subtle. Studying data science will help you understand how to take the raw data, analyse it, connect the dots and tell a story often via several visualizations and studying machine learning along with it will make you a specialist of artificial intelligence. Altogether this data science and machine learning course makes you an amazing Data Science and Machine Learning Engineer.

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Key Features
  • Lifetime Access

  • CloudLabs

  • 24x7 Support

  • Real-time code analysis and feedback

  • 100% Money Back Guarantee

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What is Data Science & Machine Learning Course about?

SpringPeople's Data Science and Machine Learning course will help you master the data science and analytics using different machine learning techniques and further gain deep understanding in data manipulation using R , also get introduced to hadoop architecture . 

What are the objectives of Data Science & Machine Learning Course ?

At the end of our data science and machine learning training, you will be able to

  • Manipulate and Visualise data using machine learning techniques
  • Write, optimize java code using Hadoop Framework
Available Training Modes

Live Online Training

18 Hours

Classroom Training


3 Days

Who is Data Science & Machine Learning Course for?

  • Anyone who wants to add Data Science and ML skills to their profile
  • Teams getting started on Data Science and ML project
  • What are the prerequisites for Data Science & Machine Learning Course?

    Good to have

    - This machine learning and data science course is appropriate for developers, who wish to write, maintain and/or optimize Java code using rnrnHadoop framework. A background in Java is required. Hands on experience on writing Java programs using rnrnEclipse editor would be a plus

    Course Outline

    • Day 1
      Introduction to Data Science
      • Introduction
      • Understanding Big Data
      • - Understand how different companies use big data for their business need
      • Big Data Challanges
      • Introduction to Data Science
      • Types of Data Scientists
      • Data Science Components
      • Data Science Use Cases
      • Introduction to R and Hadoop
      • R and Hadoop Integration
      • Machine Learning with Mahout
    • Hadoop Architecture
      HDFS & MapReduce
      • HDFS- Hadoop Distributed File System
      • Assumptions and Goals
      • CAP principle
      • Anatomy of Hadoop Cluster
      • Anatomy of a File Write
      • Anatomy of a File Read
      • MapReduce Framework Architecture
      • Hadoop Processes
      • Understanding Various configuration Properties of Hadoop
    • Day 2
      Data Manipulation Using R
      • Introduction to R
      • Describe why R is Used?
      • Implement R programing concepts
      • Learn Data Import techniques
      • Analyze the processing of the Data
    • Statistics and Probability
      • Observation and Experiments
      • Sampling Methods
      • Quantitative Variables
      • Skewness,Modality and Measures of Center
      • Variance, Standard Deviation, Interquartile Range
      • Probability Rules
      • - Disjoint,Non Disjoint events, Independence
      • Conditional Probability
      • Probability Distributions
    • Machine Learning Techniques - Part 1
      • Understand Machine Learning
      • - Use Cases Walkthrough
      • Machine Learning Techniques
      • Describe Clustering
      • Analyze Clustering Scenarios using Clustering Algorithms
      • Learn TF-IDF and cosine Similarity
    • Day 3
      Machine Learning Techniques - Part 2
      • Understand Supervised Learning Technique
      • Classification
      • Recommendation
      • Learn Decision Tree Classifier
      • - Implement how various Decision Tree algorithms work.
      • Implement Application of Techniques on a smaller datasets for better understanding using R.
    • Machine Learning Techniques - Part 3
      • Understand Unsupervised Learning Technique
      • Understand the implementation of Random Forest Classifier
      • Understand the implementation of Na-ve Bayer's Classifier
      • Apply both techniques on smaller datasets using R
      • Understand Association Rule Mining
    • Intergrating R with Hadoop
      • Understand the need for R integration with Hadoop
      • Learn the ways to integrate R and Hadoop
      • Understand the usage of RHadoop package
      • Perform R integration with Hadoop and Run MapReduce examples
    • Day 4
      Mahout Introduction and Algorithm Implementation
      • Understand Mahout
      • Gain insight on implementing Machine Learning with Mahout
      • Understand Learning, Classification and Clustering techniques with Mahout
      • Implement Recommendation technique and Frequent Pattern Mining in Mahout
    • Advanced Mahout Algorithms , Parallel processing and Data Visualization
      • Understand Mahout Algorithms and Parallel proicessing
      • Learn Advanced techniques in R
      • Implement Parallel Random Forest
      • Understand Data Visualization

    Who is the instructor for this training?

    The trainer for this Data Science & Machine Learning Training has extensive experience in this domain, including years of experience training & mentoring professionals.