Udemy - Machine Learning for BI, PART 2 - Classification Modeling

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[ FreeCourseWeb.com ] Udemy - Machine Learning for BI, PART 2 - Classification Modeling
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 01 Getting Started
    • 001 Course Structure & Outline.en.srt (2.9 KB)
    • 001 Course Structure & Outline.mp4 (29.8 MB)
    • 002 READ ME_ Important Notes for New Students.html (5.3 KB)
    • 003 About this Series.en.srt (3.2 KB)
    • 003 About this Series.mp4 (9.4 MB)
    • 004 DOWNLOAD_ Course Resources.html (1.6 KB)
    • 004 Machine Learning Part 2 - Classification.pdf (3.5 MB)
    • 004 Maven_ML_Demos_Part_2.xlsx (252.2 KB)
    • 005 Setting Expectations.en.srt (4.3 KB)
    • 005 Setting Expectations.mp4 (14.9 MB)
    02 Intro to Classification
    • 001 Supervised vs. Unsupervised Learning.en.srt (3.0 KB)
    • 001 Supervised vs. Unsupervised Learning.mp4 (8.4 MB)
    • 002 Classification vs. Regression.en.srt (3.3 KB)
    • 002 Classification vs. Regression.mp4 (8.4 MB)
    • 003 RECAP_ Key Concepts.en.srt (5.3 KB)
    • 003 RECAP_ Key Concepts.mp4 (14.9 MB)
    • 004 Classification 101.en.srt (5.9 KB)
    • 004 Classification 101.mp4 (17.2 MB)
    • 005 Classification Workflow.en.srt (5.1 KB)
    • 005 Classification Workflow.mp4 (12.4 MB)
    • 006 Feature Engineering.en.srt (5.2 KB)
    • 006 Feature Engineering.mp4 (16.6 MB)
    • 007 Data Splitting.en.srt (2.4 KB)
    • 007 Data Splitting.mp4 (8.3 MB)
    • 008 Overfitting.en.srt (5.6 KB)
    • 008 Overfitting.mp4 (16.7 MB)
    03 Classification Models
    • 001 Common Classification Models.en.srt (1.8 KB)
    • 001 Common Classification Models.mp4 (5.9 MB)
    • 002 Intro to K-Nearest Neighbors (KNN).en.srt (1.7 KB)
    • 002 Intro to K-Nearest Neighbors (KNN).mp4 (5.3 MB)
    • 003 KNN Examples.en.srt (6.6 KB)
    • 003 KNN Examples.mp4 (18.3 MB)
    • 004 CASE STUDY_ KNN.en.srt (14.2 KB)
    • 004 CASE STUDY_ KNN.mp4 (71.4 MB)
    • 005 Intro to Naïve Bayes.en.srt (2.4 KB)
    • 005 Intro to Naïve Bayes.mp4 (7.2 MB)
    • 006 Naïve Bayes _ Frequency Tables.en.srt (3.5 KB)
    • 006 Naïve Bayes _ Frequency Tables.mp4 (8.6 MB)
    • 007 Naïve Bayes _ Conditional Probability.en.srt (7.9 KB)
    • 007 Naïve Bayes _ Conditional Probability.mp4 (24.8 MB)
    • 008 CASE STUDY_ Naïve Bayes.en.srt (11.1 KB)
    • 008 CASE STUDY_ Naïve Bayes.mp4 (38.1 MB)
    • 009 Intro to Decision Trees.en.srt (2.7 KB)
    • 009 Intro to Decision Trees.mp4 (9.1 MB)
    • 010 Decision Trees _ Entropy 101.en.srt (3.9 KB)
    • 010 Decision Trees _ Entropy 101.mp4 (12.3 MB)
    • 011 Entropy & Information Gain.en.srt (6.5 KB)
    • 011 Entropy & Information Gain.mp4 (19.6 MB)
    • 012 Decision Tree Examples.en.srt (7.5 KB)
    • 012 Decision Tree Examples.mp4 (22.6 MB)
    • 013 Random Forests.en.srt (1.7 KB)
    • 013 Random Forests.mp4 (7.4 MB)
    • 014 CASE STUDY_ Decision Trees.en.srt (11.9 KB)
    • 014 CASE STUDY_ Decision Trees.mp4 (43.1 MB)
    • 015 Intro to Logistic Regression.en.srt (2.7 KB)
    • 015 Intro to Logistic Regression.mp4 (9.4 MB)
    • 016 Logistic Regression Example.en.srt (3.6 KB)
    • 016 Logistic Regression Example.mp4 (10.6 MB)
    • 017 False Positives vs. False Negatives.en.srt (4.2 KB)
    • 017 False Positives vs. False Negatives.mp4 (15.1 MB)
    • 018 Logistic Regression Equation.en.srt (2.6 KB)
    • 018 Logistic Regression Equation.mp4 (7.7 MB)
    • 019 The Likelihood Function.en.srt (5.2 KB)
    • 019 The Likelihood Function.mp4 (21.9 MB)
    • 020 Multivariate Logistic Regression.en.srt (3.6 KB)
    • 020 Multivariate Logistic Regression.mp4 (15.4 MB)
    • 021 CASE STUDY_ Logistic Regression.en.srt (11.7 KB)
    • 021 CASE STUDY_ Logistic Regression.mp4 (42.2 MB)
    • 022 Intro to Sentiment Analysis.en.srt (2.9 KB)
    • 022 Intro to Sentiment Analysis.mp4 (12.5 MB)
    • 023 Cleaning Text Data.en.srt (2.6 KB)
    • 023 Cleaning Text Data.mp4 (9.6 MB)
    • 024 _Bag of Words_ Analysis.en.srt (6.1 KB)
    • 024 _Bag of Words_ Analysis.mp4 (20.2 MB)
    • 025 CASE STUDY_ Sentiment Analysis.en.srt (9.6 KB)
    • 025 CASE STUDY_ Sentiment Analysis.mp4 (43.4 MB)
    04 Model Selection & Tuning
    • 001 Intro to Selection & Tuning.en.srt (1.4 KB)
    • 001 Intro to Selection & Tuning.mp4 (4.2 MB)
    • 002 Hyperparameters.en.srt (4.7 KB)
    • 002 Hyperparameters.mp4 (13.8 MB)
    • 003 Imbalanced Classes.en.srt (5.0 KB)
    • 003 Imbalanced Classes.mp4 (15.5 MB)
    • 004 Confusion Matrix.en.srt (3.3 KB)
    • 004 Confusion Matrix.mp4 (9.9 MB)
    • 005 Accuracy, Precision & Recall.en.srt (3.6 KB)
    • 005 Accuracy, Precision & Recall.mp4 (10.2 MB)
    • 006 Multi-class Confusion Matrix.en.srt (3.2 KB)
    • 006 Multi-class Confusion Matrix.mp4 (10.3 MB)
    • 007 Multi-class Scoring.en.srt (6.1 KB)
    • 007 Multi-class Scoring.mp4 (19.7 MB)
    • 008 Model Selection.en.srt (2.5 KB)
    • 008 Model Selection.mp4 (8.5 MB)
    • 009 Model Drift.en.srt (1.7 KB)
    • 009 Model Drift.mp4 (4.7 MB)
    05 Wrapping Up
    • 001 Looking Ahead to Part 3.en.srt (0.7 KB)
    • 001 Looking Ahead to Part 3.mp4 (3.4 MB)
    • 002 BONUS LECTURE.html (7.6 KB)
    • Bonus Resources.txt (0.3 KB)

Description

Machine Learning for BI, PART 2: Classification Modeling



MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 49 lectures (2h 31m) | Size: 566.4 MB
Demystify Machine Learning and build foundational Data Science skills for classification & prediction, without any code!
What you'll learn:
Build foundational machine learning & data science skills, without writing complex code
Use intuitive, user-friendly tools like Microsoft Excel to introduce & demystify machine learning tools & techniques
Enrich datasets by using feature engineering techniques like one-hot encoding, scaling, and discretization
Predict categorical outcomes using classification models like K-nearest neighbors, naïve bayes, decision trees, and more
Apply techniques for selecting & tuning classification models to optimize performance, reduce bias, and minimize drift
Calculate metrics like accuracy, precision and recall to measure model performance

Requirements
This is a beginner-friendly course (no prior knowledge or math/stats background required)
We'll use Microsoft Excel (Office 365) for some course s, but participation is optional
This is PART 2 of our Machine Learning for BI series (we recommend taking PART 1: Data Profiling & QA first)

Description
If you're excited to explore Data Science & Machine Learning but anxious about learning complex programming languages or intimidated by terms like "naive bayes", "logistic regression", "KNN" and "decision trees", you're in the right place.

This course is PART 2 of a 4-PART SERIES designed to help you build a strong, foundational understanding of Machine Learning:



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Udemy - Machine Learning for BI, PART 2 - Classification Modeling

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Download torrent
762.6 MB
seeders:11
leechers:14
Udemy - Machine Learning for BI, PART 2 - Classification Modeling

Torrent hash: A6A20E862EEE1D07D3E0A1C9703A72E6C1BF27AC