Projects
Assignments
Overview
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 .
Objectives
At the end of Machine Learning with Data Science training course, participants will be able to
Prerequisites
- 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 Hadoop framework
- Hands on experience on writing Java programs using Eclipse editor would be a plus
Course Outline
Introduction to Data Scienceedforce2021-06-30T15:08:45+05:30
- 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 & MapReduceedforce2021-06-30T15:09:40+05:30
- 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
Data Manipulation Using Redforce2021-06-30T15:11:34+05:30
- Introduction to R
- Describe why R is Used?
- Implement R programing concepts
- Learn Data Import techniques
- Analyze the processing of the Data
Statistics and Probabilityedforce2021-06-30T15:11:41+05:30
- 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 Techniquesedforce2021-06-30T15:11:52+05:30
- 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
Intergrating R with Hadoopedforce2021-06-30T15:12:42+05:30
- 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
Mahout Introduction and Algorithm Implementationedforce2021-06-30T15:12:49+05:30
- 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 Visualizationedforce2021-06-30T15:13:14+05:30
- Understand Mahout Algorithms and Parallel proicessing
- Learn Advanced techniques in R
- Implement Parallel Random Forest
- Understand Data Visualization
