A-Z Machine Learning using Azure Machine Learning
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       Video Length : 16h00m0s
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       Tasks Number : 108
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       Students Enrolled : 1956
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       Medium Level
  • Curriculum
  • 1. Basics of Machine Learning
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      What You Will Learn in This Section
      10m0s
    • videocam
      The course slides for all sections
      10m0s
    • videocam
      Important Message About Udemy Reviews
      10m0s
    • videocam
      Why Machine Learning is the Future?
      10m0s
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      What is Machine Learning?
      10m0s
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      Understanding various aspects of data - Type, Variables, Category
      10m0s
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      Common Machine Learning Terms - Probability, Mean, Mode, Median, Range
      10m0s
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      Types of Machine Learning Models - Classification, Regression, Clustering etc
      10m0s
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      Basics of Machine Learning
      10m0s
  • 2. Getting Started with Azure ML
    • videocam
      What You Will Learn in This Section?
      10m0s
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      What is Azure ML and high level architecture.
      10m0s
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      Creating a Free Azure ML Account
      10m0s
    • videocam
      Azure ML Studio Overview and walk-through
      10m0s
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      Azure ML Experiment Workflow
      10m0s
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      Azure ML Cheat Sheet for Model Selection
      10m0s
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      Getting Started with AzureML
      10m0s
  • 3. Data Processing
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      Data Input-Output - Upload Data
      10m0s
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      Data Input-Output - Convert and Unpack
      10m0s
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      Data Input-Output - Import Data
      10m0s
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      Data Transform - Add Rows/Columns, Remove Duplicates, Select Columns
      10m0s
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      Apply SQL Transformation, Clean Missing Data, Edit Metadata
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      Sample and Split Data - Partition or Sample, Train and Test Data
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      Data Processing
      10m0s
  • 4. Classification
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      Logistic Regression - What is Logistic Regression?
      10m0s
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      Logistic Regression - Build Two-Class Loan Approval Prediction Model
      10m0s
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      Logistic Regression - Understand Parameters and Their Impact
      10m0s
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      Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score
      10m0s
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      Logistic Regression - Model Selection and Impact Analysis
      10m0s
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      Logistic Regression - Build Multi-Class Wine Quality Prediction Model
      10m0s
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      Decision Tree - What is Decision Tree?
      10m0s
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      Decision Tree - Ensemble Learning - Bagging and Boosting
      10m0s
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      Decision Tree - Parameters - Two Class Boosted Decision Tree
      10m0s
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      Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction
      10m0s
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      Decision Forest - Parameters Explained
      10m0s
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      Two Class Decision Forest - Adult Census Income Prediction
      10m0s
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      Decision Tree - Multi Class Decision Forest IRIS Data
      10m0s
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      SVM - What is Support Vector Machine?
      10m0s
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      SVM - Adult Census Income Prediction
      10m0s
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      Classification Quiz
      10m0s
  • 5. Hyperparameter Tuning
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      Tune Hyperparameter for Best Parameter Selection
      10m0s
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      Hyperparameter Tuning
      10m0s
  • 6. Deploy Webservice
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      Azure ML Webservice - Prepare the experiment for webservice
      10m0s
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      Deploy Machine Learning Model As a Web Service
      10m0s
    • videocam
      Use the Web Service - Example of Excel
      10m0s
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      AzureML Web Service
      10m0s
  • 7. Regression Analysis
    • videocam
      What is Linear Regression?
      10m0s
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      Regression Analysis - Common Metrics
      10m0s
    • videocam
      Linear Regression model using OLS
      10m0s
    • videocam
      Linear Regression - R Squared
      10m0s
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      Gradient Descent
      10m0s
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      Linear Regression: Online Gradient Descent
      10m0s
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      Experiment Online Gradient
      10m0s
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      Decision Tree - What is Regression Tree?
      10m0s
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      Decision Tree - What is Boosted Decision Tree Regression?
      10m0s
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      Decision Tree - Experiment Boosted Decision Tree
      10m0s
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      Regression Analysis
      10m0s
  • 8. Clustering
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      What is Cluster Analysis?
      10m0s
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      Cluster Analysis Experiment 1
      10m0s
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      Cluster Analysis Experiment 2 - Score and Evaluate
      10m0s
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      Clustering or Cluster Analysis
      10m0s
  • 9. Data Processing - Solving Data Processing Challenges
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      Section Introduction
      10m0s
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      How to Summarize Data?
      10m0s
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      Summarize Data - Experiment
      10m0s
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      Outliers Treatment - Clip Values
      10m0s
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      Outliers Treatment - Clip Values
      10m0s
    • videocam
      Clean Missing Data with MICE
      10m0s
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      Clean Missing Data with MICE
      10m0s
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      SMOTE - Create New Synthetic Observations
      10m0s
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      SMOTE
      10m0s
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      Data Normalization - Scale and Reduce
      10m0s
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      Data Normalization
      10m0s
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      PCA - What is PCA and Curse of Dimensionality?
      10m0s
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      Principal Component Analysis
      10m0s
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      Join Data - Join Multiple Datasets based on common keys
      10m0s
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      Join Data - Experiment
      10m0s
  • 10. Feature Selection - Select a subset of Variables or features with highest impact
    • videocam
      Feature Selection - Section Introduction
      10m0s
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      Pearson Correlation Coefficient
      10m0s
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      Chi Square Test of Independence
      10m0s
    • videocam
      Kendall Correlation Coefficient
      10m0s
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      Spearman's Rank Correlation
      10m0s
    • videocam
      Comparison Experiment for Correlation Coefficients
      10m0s
    • videocam
      Filter Based Selection - AzureML Experiment
      10m0s
    • videocam
      Fisher Based LDA - Intuition
      10m0s
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      Fisher Based LDA - Experiment
      10m0s
  • 11. Recommendation System
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      What is a Recommendation System?
      10m0s
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      Data Preparation using Recommender Split
      10m0s
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      What is Matchbox Recommender and Train Matchbox Recommender
      10m0s
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      How to Score the Matchbox Recommender?
      10m0s
    • videocam
      Restaurant Recommendation Experiment
      10m0s
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      Understanding the Matchbox Recommendation Results
      10m0s
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      Recommendation System
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  • 12. Text Analytics and Natural Language Processing
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      What is Text Analytics or Natural Language Processing?
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      Text Pre-Processing
      10m0s
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      Bag Of Words and N-Gram Models for Text features
      10m0s
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      Feature Hashing
      10m0s
    • videocam
      Classify Customer Complaints using Text Analytics
      10m0s
Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

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A-Z Machine Learning using Azure Machine Learning


A-Z Machine Learning using Azure Machine Learning

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