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Machine Learning With Big Data 

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Machine Learning With Big Data
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Coursera 
Overview

Duration

22 hours

Start from

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Total fee

Free

Mode of learning

Online

Official Website

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Credential

Certificate

Machine Learning With Big Data
 at 
Coursera 
Highlights

  • Shareable Certificate Earn a Certificate upon completion
  • 100% online Start instantly and learn at your own schedule.
  • Course 4 of 6 in the Big Data Specialization
  • Flexible deadlines Reset deadlines in accordance to your schedule.
  • Approx. 22 hours to complete
  • English Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, English, Spanish
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Machine Learning With Big Data
 at 
Coursera 
Course details

More about this course
  • Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.
  • At the end of the course, you will be able to:
  • ? Design an approach to leverage data using the steps in the machine learning process.
  • ? Apply machine learning techniques to explore and prepare data for modeling.
  • ? Identify the type of machine learning problem in order to apply the appropriate set of techniques.
  • ? Construct models that learn from data using widely available open source tools.
  • ? Analyze big data problems using scalable machine learning algorithms on Spark.
  • Software Requirements:
  • Cloudera VM, KNIME, Spark
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Machine Learning With Big Data
 at 
Coursera 
Curriculum

Welcome

Welcome to Machine Learning With Big Data

Summary of Big Data Integration and Processing

Machine Learning Overview

Categories Of Machine Learning Techniques

Machine Learning Process

Goals and Activities in the Machine Learning Process

CRISP-DM

Scaling Up Machine Learning Algorithms

Tools Used in this Course

Slides: Machine Learning Overview and Applications

Downloading, Installing and Using KNIME

Downloading and Installing the Cloudera VM Instructions (Windows)

Downloading and Installing the Cloudera VM Instructions (Mac)

Instructions for Downloading Hands On Datasets

Instructions for Starting Jupyter

PDFs of Readings for Week 1 Hands-On

Machine Learning Overview

Data Exploration

Data Terminology

Data Exploration

Data Exploration through Summary Statistics

Data Exploration through Plots

Exploring Data with KNIME Plots

Data Exploration in Spark

Slides: Data Exploration Overview and Terminology

Description of Daily Weather Dataset

Exploring Data with KNIME Plots

Data Exploration in Spark

PDFs of Activities for Data Exploration Hands-On Readings

Data Exploration

Data Exploration in KNIME and Spark Quiz

Data Preparation

Data Quality

Addressing Data Quality Issues

Feature Selection

Feature Transformation

Dimensionality Reduction

Handling Missing Values in KNIME

Handling Missing Values in Spark

Slides: Data Preparation for Machine Learning

Handling Missing Values in KNIME

Handling Missing Values in Spark

PDFs for Data Preparation Hands-On Readings

Data Preparation

Handling Missing Values in KNIME and Spark Quiz

Classification

Classification

Building and Applying a Classification Model

Classification Algorithms

k-Nearest Neighbors

Decision Trees

Naïve Bayes

Classification using Decision Tree in KNIME

Classification in Spark

Slides: What is Classification?

Slides: Classification Algorithms

Classification using Decision Tree in KNIME

Interpreting a Decision Tree in KNIME

Instructions for Changing the Number of Cloudera VM CPUs

Classification in Spark

PDFs for Classification Hands-On Readings

Classification

Classification in KNIME and Spark Quiz

Evaluation of Machine Learning Models

Generalization and Overfitting

Overfitting in Decision Trees

Using a Validation Set

Metrics to Evaluate Model Performance

Confusion Matrix

Evaluation of Decision Tree in KNIME

Evaluation of Decision Tree in Spark

Slides: Overfitting: What is it and how would you prevent it?

Slides: Model evaluation metrics and methods

Evaluation of Decision Tree in KNIME

Completed KNIME Workflows

Evaluation of Decision Tree in Spark

Comparing Classification Results for KNIME and Spark

PDFs for Evaluation of Machine Learning Models Hands-On Readings

Model Evaluation

Model Evaluation in KNIME and Spark Quiz

Regression, Cluster Analysis, and Association Analysis

Regression Overview

Linear Regression

Cluster Analysis

k-Means Clustering

Association Analysis

Association Analysis in Detail

Machine Learning With Big Data - Final Remarks

Cluster Analysis in Spark

Slides: Regression

Slides: Cluster Analysis

Slides: Association Analysis

Description of Minute Weather Dataset

Cluster Analysis in Spark

PDFs of Cluster Analysis in Spark Hands-On Readings

Regression, Cluster Analysis, & Association Analysis

Cluster Analysis in Spark Quiz

Machine Learning With Big Data
 at 
Coursera 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

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    Students Ratings & Reviews

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    Sowmith Reddy Jonnada
    Machine Learning With Big Data
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    5
    Learning Experience: The basic Python data structures in Python include list, set, tuples, and dictionary. Each of the data structures is unique in its own way. Data structures are “containers” that organize and group data according to type. The data structures differ based on mutability and order.
    Faculty: Research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions. Learn, implement, and use different Data Structures. Learn, implement and use different Algorithms. Become a better developer by mastering computer science fundamentals. Learn everything you need to ace difficult coding interviews. Cracking the Coding Interview with 100+ questions with explanations.
    Course Support: Data structures in Python allowed me to organize, store and manage data so that it can be accessed and modified efficiently. Python has four types of built-in data structures, list, tuple, set, and dictionary. Python also has user-defined data structures.
    Reviewed on 23 Dec 2022Read More
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    Machine Learning With Big Data
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