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[FTUForum.com] [UDEMY] A-Z Machine Learning using Azure Machine Learning (AzureML) [FTU]

[FTUForum.com] [UDEMY] A-Z Machine Learning using Azure Machine Learning (AzureML) [FTU]

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Description




Hands on Machine Learning using Azure ML: Azure Machine Learning Studio to Advance ML Algorithms. No Coding Required



Created by: Jitesh Khurkhuriya

Last updated: 2/2019

Language: English

Caption (CC): Included

Torrent Contains: 230 Files, 13 Folders

Course Source: https://www.udemy.com/machine-learning-using-azureml/



What you'll learn



• Master Data Science and Machine Learning Models using Azure ML.

• Understand the concepts and intuition of Machine Learning algorithms

• Build Machine Learning models within minutes

• Choose the correct Machine Learning Algorithm using the cheatsheet

• Deploy production grade Machine Learning algorithms

• Deploy Machine Learning webservices in the simplest form possible including excel

• Bring in great value to business you manage



Requirements



• Basic Math is good enough. This course does not require background in Data Science. Will be great if you have one.

• Free or paid subscription to Microsoft Azure is required. It may ask for Phone and/or Credit Card for verification



Description



Machine Learning is one of the hottest and top paying skills. It's also one of the most interesting field to work on.



In this course of Machine Learning using Azure Machine Learning, we will make it even more exciting and fun to learn, create and deploy machine learning models. We will go through every concept in depth. This course not only teaches basic but also the advance techniques of Data processing, Feature Selection and Parameter Tuning which an experienced and seasoned Data Science expert typically deploys. Armed with these techniques, in a very short time, you will be able to match the results that an experienced data scientist can achieve.



This course will help you prepare for the entry to this hot career path of Machine Learning.



This course has more than 80 lectures and is over 11 hours in content.  That simply means, we go through the details of Data Science and Machine Learning along with its implementation. Almost every topic has a hands-on lab that you can practice. I have dealt with almost all scenarios during my tenure with various governments across the world and Fortune 500 companies.



I am committed to and invested in your success. I have always provided answers to all the questions and not a single question remains unanswered for more than a few days. The course is also regularly updated with newer features.



Learning data science and then further deploying Machine Learning Models have been difficult in the past. To make it easier, I have explained the concepts using very simple and day-to-day examples. Azure ML is Microsoft's way of democratizing Machine Learning. We will use this revolutionary tool to implement our models. Once learnt, you will be able to create and deploy machine learning models in less than an hour using Azure Machine Learning Studio.



Azure Machine Learning Studio is a great tool to learn to build advance models without writing a single line of code using simple drag and drop functionality. Azure Machine Learning (AzureML) is considered as a game changer in the domain of Data Science and Machine Learning.



This course has been designed keeping in mind entry level Data Scientists or no background in programming. This course will also help the data scientists to learn the AzureML tool. You can skip some of the initial lectures or run them at 2x speed, if you are already familiar with the concepts or basics of Machine Learning.



The course is very hands on and you will be able to develop your own advance models while learning,



• Advance Data Processing methods

• Statistical Analysis of the data using Azure Machine Learning Modules

• MICE or Multiple Imputation By Chained Equation

• SMOTE or Synthetic Minority Oversampling Technique

• PCA; Principal Component Analysis

• Two class and multiclass classifications

• Logistic Regression

• Decision Trees

• Linear Regression

• Support Vector Machine (SVM)

• Understanding how to evaluate and score models

• Detailed Explanation of input parameters to the models

• How to choose the best model using Hyperparameter Tuning

• Deploy your models as a webservice using Azure Machine Learning Studio

• Cluster Analysis

• K-Means Clustering

• Feature selection using Filter-based as well as Fisher LDA of AzureML Studio

• Recommendation system using one of the most powerful recommender of Azure Machine Learning

• All the slides and reference material for offline reading



You will learn and master, all of the above even if you do not have any prior knowledge of programming.



This course is a complete Machine Learning course with basics covered. We will not only build the models but also explain various parameters of all those models and where we can apply them.



In this course, we will start with some basic terms which are used very frequently in machine learning.



I will also explain:



• What is Machine Learning and some real world examples.

• Azure Machine Learning Introduction

• Provide an overview of Azure Machine Learning Studio and high level architecture.



We would also look at:



• Steps for building an ML model.

• Supervised and Unsupervised learning

• Understanding the data and pre-processing

• Different model types

• The AzureML Cheat Sheet.

• How to use Classification and Regression

• What is clustering or cluster analysis



KDNuggets one of the leading forums on Data Science calls Azure Machine Learning as the next big thing in Machine Learning. It further goes on to say, "people without data science background can also build data models through drag-and-drop gestures and simple data flow diagrams."



Azure Machine Learning's library has many pre-built models that you can re-use as well as deploy them.



This course will also be a great help in preparing for the Microsoft 70-774 exam-Perform Data Science on Cloud using Azure Machine Learning. It covers almost all the topics of Azure Machine Learning.



So see you inside the course.



Who this course is for:



• Developers who want to start a career in or wants to learn about the exciting domain of Data Science and Machine Learning

• Business Analysts who want to apply Data Science to solve business problems

• Functional Experts who can take help of Machine Learning and build/test their hypothesis quickly

• Anyone who wants to learn Machine Learning

• Students and non-technical professionals who want to start a career in Machine Learning

• Business Process Managers who want to automate their processes or decision making

• Marketing professionals who want to apply machine learning for better predictions of sales, conversion, churn.



For More Udemy Free Courses >>> https://ftuforum.com/

For more Lynda and other Courses >>> https://www.freecoursesonline.me/

Our Forum for discussion >>> https://discuss.ftuforum.com/








File list
  • [FTUForum.com] [UDEMY] A-Z Machine Learning using Azure Machine Learning (AzureML) [FTU]
  • 01. Basics of Machine Learning/1. What You Will Learn in This Section.mp4 18.9 MB
  • 01. Basics of Machine Learning/1. What You Will Learn in This Section.vtt 2.3 KB
  • 01. Basics of Machine Learning/2. The course slides for all sections.html 336 B
  • 01. Basics of Machine Learning/2.1 Section 01 - Basics of Machine Learning.pdf.pdf 1.8 MB
  • 01. Basics of Machine Learning/2.10 Section 09 - Data Processing.pdf.pdf 2.8 MB
  • 01. Basics of Machine Learning/2.11 Section 04 - Classification - 002 - Decision Tree.pdf.pdf 3.4 MB
  • 01. Basics of Machine Learning/2.12 Section 11 - Recommendation System.pdf.pdf 3.1 MB
  • 01. Basics of Machine Learning/2.13 Section - Text Analytics.pdf.pdf 2 MB
  • 01. Basics of Machine Learning/2.14 Section 03 - Data Pre-processing.pdf.pdf 1 MB
  • 01. Basics of Machine Learning/2.2 Section 06 - Deploy Webservice.pdf.pdf 702.4 KB
  • 01. Basics of Machine Learning/2.3 Section 04 - Classification - 001 - Logistic Regression.pdf.pdf 1.4 MB
  • 01. Basics of Machine Learning/2.4 Section 02 - Getting Started with AzureML.pdf.pdf 2.7 MB
  • 01. Basics of Machine Learning/2.5 Section 04 - Classification - 003 - SVM.pdf.pdf 1.1 MB
  • 01. Basics of Machine Learning/2.6 Section 08 - Clustering.pdf.pdf 1.5 MB
  • 01. Basics of Machine Learning/2.7 Section 05 - Tune Hyperparameter.pdf.pdf 1.2 MB
  • 01. Basics of Machine Learning/2.8 Section 07 - Regression.pdf.pdf 2.8 MB
  • 01. Basics of Machine Learning/2.9 Section 10 - Feature Selection.pdf.pdf 2.9 MB
  • 01. Basics of Machine Learning/3. Important Message About Udemy Reviews.mp4 18.9 MB
  • 01. Basics of Machine Learning/3. Important Message About Udemy Reviews.vtt 3.8 KB
  • 01. Basics of Machine Learning/4. Why Machine Learning is the Future.mp4 68.7 MB
  • 01. Basics of Machine Learning/4. Why Machine Learning is the Future.vtt 9.2 KB
  • 01. Basics of Machine Learning/5. What is Machine Learning.mp4 54.7 MB
  • 01. Basics of Machine Learning/5. What is Machine Learning.vtt 9.7 KB
  • 01. Basics of Machine Learning/6. Understanding various aspects of data - Type, Variables, Category.mp4 13.6 MB
  • 01. Basics of Machine Learning/6. Understanding various aspects of data - Type, Variables, Category.vtt 7.1 KB
  • 01. Basics of Machine Learning/7. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range.mp4 13.3 MB
  • 01. Basics of Machine Learning/7. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range.vtt 7.5 KB
  • 01. Basics of Machine Learning/8. Types of Machine Learning Models - Classification, Regression, Clustering etc.mp4 19 MB
  • 01. Basics of Machine Learning/8. Types of Machine Learning Models - Classification, Regression, Clustering etc.vtt 9.2 KB
  • 01. Basics of Machine Learning/9. Basics of Machine Learning.html 136 B
  • 02. Getting Started with Azure ML/1. What You Will Learn in This Section.mp4 13.3 MB
  • 02. Getting Started with Azure ML/1. What You Will Learn in This Section.vtt 2.1 KB
  • 02. Getting Started with Azure ML/2. What is Azure ML and high level architecture..mp4 22.9 MB
  • 02. Getting Started with Azure ML/2. What is Azure ML and high level architecture..vtt 3.5 KB
  • 02. Getting Started with Azure ML/3. Creating a Free Azure ML Account.mp4 5.4 MB
  • 02. Getting Started with Azure ML/3. Creating a Free Azure ML Account.vtt 2.2 KB
  • 02. Getting Started with Azure ML/4. Azure ML Studio Overview and walk-through.mp4 12.2 MB
  • 02. Getting Started with Azure ML/4. Azure ML Studio Overview and walk-through.vtt 4.5 KB
  • 02. Getting Started with Azure ML/5. Azure ML Experiment Workflow.mp4 13.2 MB
  • 02. Getting Started with Azure ML/5. Azure ML Experiment Workflow.vtt 6.7 KB
  • 02. Getting Started with Azure ML/6. Azure ML Cheat Sheet for Model Selection.mp4 11.3 MB
  • 02. Getting Started with Azure ML/6. Azure ML Cheat Sheet for Model Selection.vtt 5.8 KB
  • 02. Getting Started with Azure ML/6.1 microsoft-machine-learning-algorithm-cheat-sheet-v6.pdf.pdf 404.1 KB
  • 02. Getting Started with Azure ML/6.2 ml_studio_overview_v1.1.pdf.pdf 2.2 MB
  • 02. Getting Started with Azure ML/7. Getting Started with AzureML.html 136 B
  • 03. Data Processing/1. [Hands On] - Data Input-Output - Upload Data.mp4 18.6 MB
  • 03. Data Processing/1. [Hands On] - Data Input-Output - Upload Data.vtt 7.2 KB
  • 03. Data Processing/1.1 Employee Dataset - Full.csv.csv 1.9 KB
  • 03. Data Processing/2. [Hands On] - Data Input-Output - Convert and Unpack.mp4 22.1 MB
  • 03. Data Processing/2. [Hands On] - Data Input-Output - Convert and Unpack.vtt 8.1 KB
  • 03. Data Processing/2.1 Employee Dataset - Full.zip.zip 773 B
  • 03. Data Processing/3. [Hands On] - Data Input-Output - Import Data.mp4 13.1 MB
  • 03. Data Processing/3. [Hands On] - Data Input-Output - Import Data.vtt 5.7 KB
  • 03. Data Processing/3.1 Adult Dataset URL.txt.txt 74 B
  • 03. Data Processing/4. [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns.mp4 26.5 MB
  • 03. Data Processing/4. [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns.vtt 10.3 KB
  • 03. Data Processing/4.1 Employee Dataset - AC1.csv.csv 1.6 KB
  • 03. Data Processing/4.2 Employee Dataset - AR2.csv.csv 1.3 KB
  • 03. Data Processing/4.3 Employee Dataset - AC2.csv.csv 260 B
  • 03. Data Processing/4.4 Employee Dataset - AR1.csv.csv 672 B
  • 03. Data Processing/4.5 Employee Dataset - TSV.txt.txt 1.9 KB
  • 03. Data Processing/5. [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata.mp4 38.9 MB
  • 03. Data Processing/5. [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata.vtt 16.2 KB
  • 03. Data Processing/5.1 Wine Quality Dataset.csv.csv 83.7 KB
  • 03. Data Processing/5.2 SQL Statement - Wine.txt.txt 141 B
  • 03. Data Processing/6. [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data.mp4 35.5 MB
  • 03. Data Processing/6. [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data.vtt 14.5 KB
  • 03. Data Processing/7. Data Processing.html 136 B
  • 04. Classification/1. Logistic Regression - What is Logistic Regression.mp4 11.5 MB
  • 04. Classification/1. Logistic Regression - What is Logistic Regression.vtt 5.8 KB
  • 04. Classification/10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.mp4 25.2 MB
  • 04. Classification/10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.vtt 9 KB
  • 04. Classification/10.1 Bank Telemarketing.csv.csv 4.7 MB
  • 04. Classification/11. Decision Forest - Parameters Explained.mp4 5.8 MB
  • 04. Classification/11. Decision Forest - Parameters Explained.vtt 3.4 KB
  • 04. Classification/12. [Hands On] - Two Class Decision Forest - Adult Census Income Prediction.mp4 35.1 MB
  • 04. Classification/12. [Hands On] - Two Class Decision Forest - Adult Census Income Prediction.vtt 12.5 KB
  • 04. Classification/13. [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data.mp4 18.6 MB
  • 04. Classification/13. [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data.vtt 7 KB
  • 04. Classification/13.1 IRIS Dataset Link.txt.txt 74 B
  • 04. Classification/14. SVM - What is Support Vector Machine.mp4 7.1 MB
  • 04. Classification/14. SVM - What is Support Vector Machine.vtt 3.5 KB
  • 04. Classification/15. [Hands On] - SVM - Adult Census Income Prediction.mp4 13.8 MB
  • 04. Classification/15. [Hands On] - SVM - Adult Census Income Prediction.vtt 5 KB
  • 04. Classification/16. Classification Quiz.html 136 B
  • 04. Classification/2. [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model.mp4 52.2 MB
  • 04. Classification/2. [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model.vtt 19.8 KB
  • 04. Classification/2.1 Loan Approval Prediction.csv.csv 37.1 KB
  • 04. Classification/3. Logistic Regression - Understand Parameters and Their Impact.mp4 19.6 MB
  • 04. Classification/3. Logistic Regression - Understand Parameters and Their Impact.vtt 11.3 KB
  • 04. Classification/4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score.mp4 29.4 MB
  • 04. Classification/4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score.vtt 11.9 KB
  • 04. Classification/4.1 004 - Logistic Regression - Understanding the results.xlsx.xlsx 24 KB
  • 04. Classification/5. Logistic Regression - Model Selection and Impact Analysis.mp4 13.8 MB
  • 04. Classification/5. Logistic Regression - Model Selection and Impact Analysis.vtt 5 KB
  • 04. Classification/6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.mp4 19.7 MB
  • 04. Classification/6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.vtt 7.5 KB
  • 04. Classification/6.1 winequality-red.csv.csv 83.7 KB
  • 04. Classification/7. Decision Tree - What is Decision Tree.mp4 14.3 MB

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