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Machine Learning with Data Science 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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What is Machine Learning with Data Science 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 Machine Learning with Data Science 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

Who is Machine Learning with Data Science Course for?

  • Data Analyst 
  • Data Scientist who are looking to implement Predictive Modeling
  • Teams getting started on Data Science and ML project

    • What are the prerequisites for Machine Learning with Data Science Course?

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

    Available Training Modes

    Live Online Training

    18 Hours

    Classroom Training

    3 Days

    Course Outline Expand All

    Expand All

    • 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
    • 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
    • Introduction to R
    • Describe why R is Used?
    • Implement R programing concepts
    • Learn Data Import techniques
    • Analyze the processing of the Data
    • 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
    • Understand Machine Learning
    • Use Cases Walkthrough
    • Machine Learning Techniques
    • Describe Clustering
    • Analyze Clustering Scenarios using Clustering Algorithms
    • Learn TF-IDF and cosine Similarity
    • 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.
    • 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
    • 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
    • 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
    • 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 Machine Learning with Data Science Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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