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Torrent Details For "[UDEMY] A-Z Machine Learning using Azure Machine Learning (AzureML) [FTU] GloDLS"

[UDEMY] A-Z Machine Learning using Azure Machine Learning (AzureML) [FTU] GloDLS

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Name:	[UDEMY] A-Z Machine Learning using Azure Machine Learning (AzureML) [FTU] GloDLS
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.
YouTube Video:
Category:	Tutorials
Language:	English
Total Size:	1.89 GB
Info Hash:	61135EAEEC9E31EC14F7E260CA621B96AF026AD9
Added By:	Prom3th3uS
Date Added:	2019-03-17 14:26:40
Torrent Status:	Torrent Verified

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File List:

File	Size
01. Basics of Machine Learning/1. What You Will Learn in This Section.mp4	18.92 MB
01. Basics of Machine Learning/1. What You Will Learn in This Section.vtt	2.32 kB
01. Basics of Machine Learning/2. The course slides for all sections.html	336.00 B
01. Basics of Machine Learning/2.1 Section 01 - Basics of Machine Learning.pdf.pdf	1.84 MB
01. Basics of Machine Learning/2.10 Section 09 - Data Processing.pdf.pdf	2.84 MB
01. Basics of Machine Learning/2.11 Section 04 - Classification - 002 - Decision Tree.pdf.pdf	3.40 MB
01. Basics of Machine Learning/2.12 Section 11 - Recommendation System.pdf.pdf	3.09 MB
01. Basics of Machine Learning/2.13 Section - Text Analytics.pdf.pdf	2.02 MB
01. Basics of Machine Learning/2.14 Section 03 - Data Pre-processing.pdf.pdf	1.01 MB
01. Basics of Machine Learning/2.2 Section 06 - Deploy Webservice.pdf.pdf	702.45 kB
01. Basics of Machine Learning/2.3 Section 04 - Classification - 001 - Logistic Regression.pdf.pdf	1.40 MB
01. Basics of Machine Learning/2.4 Section 02 - Getting Started with AzureML.pdf.pdf	2.68 MB
01. Basics of Machine Learning/2.5 Section 04 - Classification - 003 - SVM.pdf.pdf	1.12 MB
01. Basics of Machine Learning/2.6 Section 08 - Clustering.pdf.pdf	1.54 MB
01. Basics of Machine Learning/2.7 Section 05 - Tune Hyperparameter.pdf.pdf	1.19 MB
01. Basics of Machine Learning/2.8 Section 07 - Regression.pdf.pdf	2.81 MB
01. Basics of Machine Learning/2.9 Section 10 - Feature Selection.pdf.pdf	2.95 MB
01. Basics of Machine Learning/3. Important Message About Udemy Reviews.mp4	18.91 MB
01. Basics of Machine Learning/3. Important Message About Udemy Reviews.vtt	3.77 kB
01. Basics of Machine Learning/4. Why Machine Learning is the Future.mp4	68.72 MB
01. Basics of Machine Learning/4. Why Machine Learning is the Future.vtt	9.22 kB
01. Basics of Machine Learning/5. What is Machine Learning.mp4	54.73 MB
01. Basics of Machine Learning/5. What is Machine Learning.vtt	9.74 kB
01. Basics of Machine Learning/6. Understanding various aspects of data - Type, Variables, Category.mp4	13.61 MB
01. Basics of Machine Learning/6. Understanding various aspects of data - Type, Variables, Category.vtt	7.11 kB
01. Basics of Machine Learning/7. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range.mp4	13.31 MB
01. Basics of Machine Learning/7. Common Machine Learning Terms - Probability, Mean, Mode, Median, Range.vtt	7.46 kB
01. Basics of Machine Learning/8. Types of Machine Learning Models - Classification, Regression, Clustering etc.mp4	19.04 MB
01. Basics of Machine Learning/8. Types of Machine Learning Models - Classification, Regression, Clustering etc.vtt	9.16 kB
01. Basics of Machine Learning/9. Basics of Machine Learning.html	136.00 B
02. Getting Started with Azure ML/1. What You Will Learn in This Section.mp4	13.33 MB
02. Getting Started with Azure ML/1. What You Will Learn in This Section.vtt	2.12 kB
02. Getting Started with Azure ML/2. What is Azure ML and high level architecture..mp4	22.86 MB
02. Getting Started with Azure ML/2. What is Azure ML and high level architecture..vtt	3.47 kB
02. Getting Started with Azure ML/3. Creating a Free Azure ML Account.mp4	5.42 MB
02. Getting Started with Azure ML/3. Creating a Free Azure ML Account.vtt	2.18 kB
02. Getting Started with Azure ML/4. Azure ML Studio Overview and walk-through.mp4	12.17 MB
02. Getting Started with Azure ML/4. Azure ML Studio Overview and walk-through.vtt	4.54 kB
02. Getting Started with Azure ML/5. Azure ML Experiment Workflow.mp4	13.22 MB
02. Getting Started with Azure ML/5. Azure ML Experiment Workflow.vtt	6.74 kB
02. Getting Started with Azure ML/6. Azure ML Cheat Sheet for Model Selection.mp4	11.27 MB
02. Getting Started with Azure ML/6. Azure ML Cheat Sheet for Model Selection.vtt	5.83 kB
02. Getting Started with Azure ML/6.1 microsoft-machine-learning-algorithm-cheat-sheet-v6.pdf.pdf	404.10 kB
02. Getting Started with Azure ML/6.2 ml_studio_overview_v1.1.pdf.pdf	2.25 MB
02. Getting Started with Azure ML/7. Getting Started with AzureML.html	136.00 B
03. Data Processing/1. [Hands On] - Data Input-Output - Upload Data.mp4	18.57 MB
03. Data Processing/1. [Hands On] - Data Input-Output - Upload Data.vtt	7.17 kB
03. Data Processing/1.1 Employee Dataset - Full.csv.csv	1.85 kB
03. Data Processing/2. [Hands On] - Data Input-Output - Convert and Unpack.mp4	22.09 MB
03. Data Processing/2. [Hands On] - Data Input-Output - Convert and Unpack.vtt	8.13 kB
03. Data Processing/2.1 Employee Dataset - Full.zip.zip	773.00 B
03. Data Processing/3. [Hands On] - Data Input-Output - Import Data.mp4	13.12 MB
03. Data Processing/3. [Hands On] - Data Input-Output - Import Data.vtt	5.73 kB
03. Data Processing/3.1 Adult Dataset URL.txt.txt	74.00 B
03. Data Processing/4. [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns.mp4	26.47 MB
03. Data Processing/4. [Hands On] -Data Transform - Add RowsColumns, Remove Duplicates, Select Columns.vtt	10.32 kB
03. Data Processing/4.1 Employee Dataset - AC1.csv.csv	1.62 kB
03. Data Processing/4.2 Employee Dataset - AR2.csv.csv	1.34 kB
03. Data Processing/4.3 Employee Dataset - AC2.csv.csv	260.00 B
03. Data Processing/4.4 Employee Dataset - AR1.csv.csv	672.00 B
03. Data Processing/4.5 Employee Dataset - TSV.txt.txt	1.85 kB
03. Data Processing/5. [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata.mp4	38.91 MB
03. Data Processing/5. [Hands On] - Apply SQL Transformation, Clean Missing Data, Edit Metadata.vtt	16.23 kB
03. Data Processing/5.1 Wine Quality Dataset.csv.csv	83.73 kB
03. Data Processing/5.2 SQL Statement - Wine.txt.txt	141.00 B
03. Data Processing/6. [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data.mp4	35.52 MB
03. Data Processing/6. [Hands On] - Sample and Split Data - Partition or Sample, Train and Test Data.vtt	14.50 kB
03. Data Processing/7. Data Processing.html	136.00 B
04. Classification/1. Logistic Regression - What is Logistic Regression.mp4	11.48 MB
04. Classification/1. Logistic Regression - What is Logistic Regression.vtt	5.82 kB
04. Classification/10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.mp4	25.17 MB
04. Classification/10. [Hands On] Two-Class Boosted Decision Tree - Build Bank Telemarketing Prediction.vtt	8.99 kB
04. Classification/10.1 Bank Telemarketing.csv.csv	4.71 MB
04. Classification/11. Decision Forest - Parameters Explained.mp4	5.79 MB
04. Classification/11. Decision Forest - Parameters Explained.vtt	3.40 kB
04. Classification/12. [Hands On] - Two Class Decision Forest - Adult Census Income Prediction.mp4	35.10 MB
04. Classification/12. [Hands On] - Two Class Decision Forest - Adult Census Income Prediction.vtt	12.54 kB
04. Classification/13. [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data.mp4	18.57 MB
04. Classification/13. [Hands On] - Decision Tree - Multi Class Decision Forest IRIS Data.vtt	7.05 kB
04. Classification/13.1 IRIS Dataset Link.txt.txt	74.00 B
04. Classification/14. SVM - What is Support Vector Machine.mp4	7.13 MB
04. Classification/14. SVM - What is Support Vector Machine.vtt	3.53 kB
04. Classification/15. [Hands On] - SVM - Adult Census Income Prediction.mp4	13.83 MB
04. Classification/15. [Hands On] - SVM - Adult Census Income Prediction.vtt	4.96 kB
04. Classification/16. Classification Quiz.html	136.00 B
04. Classification/2. [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model.mp4	52.21 MB
04. Classification/2. [Hands On] -Logistic Regression - Build Two-Class Loan Approval Prediction Model.vtt	19.76 kB
04. Classification/2.1 Loan Approval Prediction.csv.csv	37.12 kB
04. Classification/3. Logistic Regression - Understand Parameters and Their Impact.mp4	19.55 MB
04. Classification/3. Logistic Regression - Understand Parameters and Their Impact.vtt	11.26 kB
04. Classification/4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score.mp4	29.42 MB
04. Classification/4. Understanding the Confusion Matrix, AUC, Accuracy, Precision, Recall and F1Score.vtt	11.86 kB
04. Classification/4.1 004 - Logistic Regression - Understanding the results.xlsx.xlsx	23.96 kB
04. Classification/5. Logistic Regression - Model Selection and Impact Analysis.mp4	13.77 MB
04. Classification/5. Logistic Regression - Model Selection and Impact Analysis.vtt	5.03 kB
04. Classification/6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.mp4	19.67 MB
04. Classification/6. [Hands On] Logistic Regression - Build Multi-Class Wine Quality Prediction Model.vtt	7.54 kB
04. Classification/6.1 winequality-red.csv.csv	83.73 kB
04. Classification/7. Decision Tree - What is Decision Tree.mp4	14.34 MB
04. Classification/7. Decision Tree - What is Decision Tree.vtt	7.02 kB
04. Classification/8. Decision Tree - Ensemble Learning - Bagging and Boosting.mp4	12.91 MB
04. Classification/8. Decision Tree - Ensemble Learning - Bagging and Boosting.vtt	6.57 kB
04. Classification/9. Decision Tree - Parameters - Two Class Boosted Decision Tree.mp4	12.10 MB
04. Classification/9. Decision Tree - Parameters - Two Class Boosted Decision Tree.vtt	5.38 kB
05. Hyperparameter Tuning/1. [Hands On] - Tune Hyperparameter for Best Parameter Selection.mp4	21.92 MB
05. Hyperparameter Tuning/1. [Hands On] - Tune Hyperparameter for Best Parameter Selection.vtt	8.66 kB
05. Hyperparameter Tuning/2. Hyperparameter Tuning.html	136.00 B
06. Deploy Webservice/1. Azure ML Webservice - Prepare the experiment for webservice.mp4	5.56 MB
06. Deploy Webservice/1. Azure ML Webservice - Prepare the experiment for webservice.vtt	2.27 kB
06. Deploy Webservice/2. [Hands On] - Deploy Machine Learning Model As a Web Service.mp4	9.18 MB
06. Deploy Webservice/2. [Hands On] - Deploy Machine Learning Model As a Web Service.vtt	9.19 MB
06. Deploy Webservice/3. [Hands On] - Use the Web Service - Example of Excel.mp4	16.58 MB
06. Deploy Webservice/3. [Hands On] - Use the Web Service - Example of Excel.vtt	6.05 kB
06. Deploy Webservice/4. AzureML Web Service.html	137.00 B
07. Regression Analysis/1. What is Linear Regression.mp4	14.03 MB
07. Regression Analysis/1. What is Linear Regression.vtt	5.26 kB
07. Regression Analysis/10. [Hands On] - Decision Tree - Experiment Boosted Decision Tree.mp4	17.28 MB
07. Regression Analysis/10. [Hands On] - Decision Tree - Experiment Boosted Decision Tree.vtt	5.67 kB
07. Regression Analysis/11. Regression Analysis.html	137.00 B
07. Regression Analysis/2. Regression Analysis - Common Metrics.mp4	12.59 MB
07. Regression Analysis/2. Regression Analysis - Common Metrics.vtt	5.57 kB
07. Regression Analysis/3. [Hands On] - Linear Regression model using OLS.mp4	91.03 MB
07. Regression Analysis/3. [Hands On] - Linear Regression model using OLS.vtt	9.71 kB
07. Regression Analysis/4. [Hands On] - Linear Regression - R Squared.mp4	10.33 MB
07. Regression Analysis/4. [Hands On] - Linear Regression - R Squared.vtt	3.82 kB
07. Regression Analysis/5. Gradient Descent.mp4	27.66 MB
07. Regression Analysis/5. Gradient Descent.vtt	9.06 kB
07. Regression Analysis/6. Linear Regression Online Gradient Descent.mp4	6.71 MB
07. Regression Analysis/6. Linear Regression Online Gradient Descent.vtt	1.96 kB
07. Regression Analysis/7. [Hands On] - Experiment Online Gradient.mp4	10.85 MB
07. Regression Analysis/7. [Hands On] - Experiment Online Gradient.vtt	3.94 kB
07. Regression Analysis/8. Decision Tree - What is Regression Tree.mp4	12.24 MB
07. Regression Analysis/8. Decision Tree - What is Regression Tree.vtt	5.49 kB
07. Regression Analysis/9. Decision Tree - What is Boosted Decision Tree Regression.mp4	4.32 MB
07. Regression Analysis/9. Decision Tree - What is Boosted Decision Tree Regression.vtt	1.78 kB
08. Clustering/1. What is Cluster Analysis.mp4	22.38 MB
08. Clustering/1. What is Cluster Analysis.vtt	9.80 kB
08. Clustering/2. [Hands On] - Cluster Analysis Experiment 1.mp4	30.92 MB
08. Clustering/2. [Hands On] - Cluster Analysis Experiment 1.vtt	11.88 kB
08. Clustering/2.1 Callcenter Data.csv.csv	831.00 B
08. Clustering/3. [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate.mp4	18.37 MB
08. Clustering/3. [Hands On] - Cluster Analysis Experiment 2 - Score and Evaluate.vtt	6.62 kB
08. Clustering/4. Clustering or Cluster Analysis.html	137.00 B
09. Data Processing - Solving Data Processing Challenges/1. Section Introduction.mp4	5.41 MB
09. Data Processing - Solving Data Processing Challenges/1. Section Introduction.vtt	2.77 kB
09. Data Processing - Solving Data Processing Challenges/10. Data Normalization - Scale and Reduce.mp4	5.33 MB
09. Data Processing - Solving Data Processing Challenges/10. Data Normalization - Scale and Reduce.vtt	2.67 kB
09. Data Processing - Solving Data Processing Challenges/11. [Hands On] - Data Normalization.mp4	5.88 MB
09. Data Processing - Solving Data Processing Challenges/11. [Hands On] - Data Normalization.vtt	2.17 kB
09. Data Processing - Solving Data Processing Challenges/12. PCA - What is PCA and Curse of Dimensionality.mp4	10.73 MB
09. Data Processing - Solving Data Processing Challenges/12. PCA - What is PCA and Curse of Dimensionality.vtt	5.54 kB
09. Data Processing - Solving Data Processing Challenges/13. [Hands On] - Principal Component Analysis.mp4	7.41 MB
09. Data Processing - Solving Data Processing Challenges/13. [Hands On] - Principal Component Analysis.vtt	3.22 kB
09. Data Processing - Solving Data Processing Challenges/14. Join Data - Join Multiple Datasets based on common keys.mp4	10.48 MB
09. Data Processing - Solving Data Processing Challenges/14. Join Data - Join Multiple Datasets based on common keys.vtt	5.47 kB
09. Data Processing - Solving Data Processing Challenges/15. [Hands On] - Join Data - Experiment.mp4	5.56 MB
09. Data Processing - Solving Data Processing Challenges/15. [Hands On] - Join Data - Experiment.vtt	2.41 kB
09. Data Processing - Solving Data Processing Challenges/15.1 EmpSalaryJC.csv.csv	110.00 B
09. Data Processing - Solving Data Processing Challenges/15.2 EmpDeptJC.csv.csv	108.00 B
09. Data Processing - Solving Data Processing Challenges/2. How to Summarize Data.mp4	11.70 MB
09. Data Processing - Solving Data Processing Challenges/2. How to Summarize Data.vtt	5.60 kB
09. Data Processing - Solving Data Processing Challenges/3. [Hands On] - Summarize Data - Experiment.mp4	8.15 MB
09. Data Processing - Solving Data Processing Challenges/3. [Hands On] - Summarize Data - Experiment.vtt	2.81 kB
09. Data Processing - Solving Data Processing Challenges/4. Outliers Treatment - Clip Values.mp4	11.49 MB
09. Data Processing - Solving Data Processing Challenges/4. Outliers Treatment - Clip Values.vtt	5.83 kB
09. Data Processing - Solving Data Processing Challenges/5. [Hands On] - Outliers Treatment - Clip Values.mp4	17.65 MB
09. Data Processing - Solving Data Processing Challenges/5. [Hands On] - Outliers Treatment - Clip Values.vtt	6.49 kB
09. Data Processing - Solving Data Processing Challenges/6. Clean Missing Data with MICE.mp4	13.05 MB
09. Data Processing - Solving Data Processing Challenges/6. Clean Missing Data with MICE.vtt	6.10 kB
09. Data Processing - Solving Data Processing Challenges/7. [Hands On] - Clean Missing Data with MICE.mp4	15.91 MB
09. Data Processing - Solving Data Processing Challenges/7. [Hands On] - Clean Missing Data with MICE.vtt	6.12 kB
09. Data Processing - Solving Data Processing Challenges/7.1 MICE Loan Dataset.csv.csv	37.12 kB
09. Data Processing - Solving Data Processing Challenges/8. SMOTE - Create New Synthetic Observations.mp4	14.21 MB
09. Data Processing - Solving Data Processing Challenges/8. SMOTE - Create New Synthetic Observations.vtt	7.27 kB
09. Data Processing - Solving Data Processing Challenges/9. [Hands On] - SMOTE.mp4	15.54 MB
09. Data Processing - Solving Data Processing Challenges/9. [Hands On] - SMOTE.vtt	4.99 kB
09. Data Processing - Solving Data Processing Challenges/9.1 LoanSMOTE.csv.csv	6.20 kB
10. Feature Selection - Select a subset of Variables or features with highest impact/1. Feature Selection - Section Introduction.mp4	7.73 MB
10. Feature Selection - Select a subset of Variables or features with highest impact/1. Feature Selection - Section Introduction.vtt	6.23 kB
10. Feature Selection - Select a subset of Variables or features with highest impact/2. Pearson Correlation Coefficient.mp4	47.22 MB
10. Feature Selection - Select a subset of Variables or features with highest impact/2. Pearson Correlation Coefficient.vtt	6.57 kB
10. Feature Selection - Select a subset of Variables or features with highest impact/3. Chi Square Test of Independence.mp4	8.28 MB
10. Feature Selection - Select a subset of Variables or features with highest impact/3. Chi Square Test of Independence.vtt	5.43 kB
10. Feature Selection - Select a subset of Variables or features with highest impact/4. Kendall Correlation Coefficient.mp4	6.71 MB
10. Feature Selection - Select a subset of Variables or features with highest impact/4. Kendall Correlation Coefficient.vtt	3.98 kB
10. Feature Selection - Select a subset of Variables or features with highest impact/5. Spearman's Rank Correlation.mp4	6.37 MB
10. Feature Selection - Select a subset of Variables or features with highest impact/5. Spearman's Rank Correlation.vtt	3.60 kB
10. Feature Selection - Select a subset of Variables or features with highest impact/6. [Hands On] - Comparison Experiment for Correlation Coefficients.mp4	13.19 MB
10. Feature Selection - Select a subset of Variables or features with highest impact/6. [Hands On] - Comparison Experiment for Correlation Coefficients.vtt	7.08 kB
10. Feature Selection - Select a subset of Variables or features with highest impact/7. [Hands On] - Filter Based Selection - AzureML Experiment.mp4	6.38 MB
10. Feature Selection - Select a subset of Variables or features with highest impact/7. [Hands On] - Filter Based Selection - AzureML Experiment.vtt	3.51 kB
10. Feature Selection - Select a subset of Variables or features with highest impact/8. Fisher Based LDA - Intuition.mp4	24.08 MB
10. Feature Selection - Select a subset of Variables or features with highest impact/8. Fisher Based LDA - Intuition.vtt	4.99 kB
10. Feature Selection - Select a subset of Variables or features with highest impact/9. [Hands On] - Fisher Based LDA - Experiment.mp4	61.14 MB
10. Feature Selection - Select a subset of Variables or features with highest impact/9. [Hands On] - Fisher Based LDA - Experiment.vtt	5.81 kB
10. Feature Selection - Select a subset of Variables or features with highest impact/9.1 Wine-Low-Medium-High.csv.csv	95.36 kB
11. Recommendation System/1. What is a Recommendation System.mp4	34.97 MB
11. Recommendation System/1. What is a Recommendation System.vtt	14.55 kB
11. Recommendation System/2. Data Preparation using Recommender Split.mp4	14.92 MB
11. Recommendation System/2. Data Preparation using Recommender Split.vtt	7.29 kB
11. Recommendation System/3. What is Matchbox Recommender and Train Matchbox Recommender.mp4	14.56 MB
11. Recommendation System/3. What is Matchbox Recommender and Train Matchbox Recommender.vtt	7.25 kB
11. Recommendation System/4. How to Score the Matchbox Recommender.mp4	10.94 MB
11. Recommendation System/4. How to Score the Matchbox Recommender.vtt	5.18 kB
11. Recommendation System/5. [Hands On] - Restaurant Recommendation Experiment.mp4	36.18 MB
11. Recommendation System/5. [Hands On] - Restaurant Recommendation Experiment.vtt	11.28 kB
11. Recommendation System/6. Understanding the Matchbox Recommendation Results.mp4	17.44 MB
11. Recommendation System/6. Understanding the Matchbox Recommendation Results.vtt	7.20 kB
11. Recommendation System/7. Recommendation System.html	137.00 B
12. Text Analytics and Natural Language Processing/1. What is Text Analytics or Natural Language Processing.mp4	40.70 MB
12. Text Analytics and Natural Language Processing/1. What is Text Analytics or Natural Language Processing.vtt	7.39 kB
12. Text Analytics and Natural Language Processing/2. Text Pre-Processing.mp4	54.61 MB
12. Text Analytics and Natural Language Processing/2. Text Pre-Processing.vtt	13.22 kB
12. Text Analytics and Natural Language Processing/3. Bag Of Words and N-Gram Models for Text features.mp4	49.96 MB
12. Text Analytics and Natural Language Processing/3. Bag Of Words and N-Gram Models for Text features.vtt	7.56 kB
12. Text Analytics and Natural Language Processing/4. Feature Hashing.mp4	75.17 MB
12. Text Analytics and Natural Language Processing/4. Feature Hashing.vtt	12.73 kB
12. Text Analytics and Natural Language Processing/5. [Hands On] - Classify Customer Complaints using Text Analytics.mp4	90.99 MB
12. Text Analytics and Natural Language Processing/5. [Hands On] - Classify Customer Complaints using Text Analytics.vtt	9.59 kB
12. Text Analytics and Natural Language Processing/5.1 two-class complaints modified.txt.txt	47.39 kB
13. Thank You and Bonus Lecture/1. Way Forward.mp4	49.18 MB
13. Thank You and Bonus Lecture/1. Way Forward.vtt	4.94 kB
13. Thank You and Bonus Lecture/1.1 Links for datasets.pdf.pdf	261.42 kB
13. Thank You and Bonus Lecture/2. Bonus Lecture.html	1.37 kB
Discuss.FTUForum.com.html	31.89 kB
FreeCoursesOnline.Me.html	108.30 kB
FTUForum.com.html	100.44 kB
How you can help Team-FTU.txt	235.00 B
Torrent Downloaded From GloDls.to.txt	84.00 B
[TGx]Downloaded from torrentgalaxy.org.txt	524.00 B

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