| 001 Course Structure & Outline.en.srt | 3.2 KB | ||
| 001 Course Structure & Outline.mp4 | 36 MB | ||
| 001 Intro to Forecasting.en.srt | 3.3 KB | ||
| 001 Intro to Forecasting.mp4 | 11 MB | ||
| 001 Intro to Model Diagnostics.en.srt | 2.6 KB | ||
| 001 Intro to Model Diagnostics.mp4 | 8.2 MB | ||
| 001 Intro to Regression Modeling.en.srt | 1.7 KB | ||
| 001 Intro to Regression Modeling.mp4 | 5.3 MB | ||
| 001 Looking Ahead to Part 4.en.srt | 1.2 KB | ||
| 001 Looking Ahead to Part 4.mp4 | 4.1 MB | ||
| 001 Supervised vs. Unsupervised Learning.en.srt | 3.4 KB | ||
| 001 Supervised vs. Unsupervised Learning.mp4 | 10.5 MB | ||
| 002 BONUS LECTURE.html | 8.1 KB | ||
| 002 Linear Relationships.en.srt | 5.7 KB | ||
| 002 Linear Relationships.mp4 | 19.9 MB | ||
| 002 READ ME_ Important Notes for New Students.html | 5.3 KB | ||
| 002 RECAP_ Key Concepts.en.srt | 3.9 KB | ||
| 002 RECAP_ Key Concepts.mp4 | 12.8 MB | ||
| 002 Sample Model Output.en.srt | 1.2 KB | ||
| 002 Sample Model Output.mp4 | 7.8 MB | ||
| 002 Seasonality.en.srt | 2.6 KB | ||
| 002 Seasonality.mp4 | 7.7 MB | ||
| 003 About This Series.en.srt | 716.8 B | ||
| 003 About This Series.mp4 | 3.2 MB | ||
| 003 Auto Correlation Function.en.srt | 3.2 KB | ||
| 003 Auto Correlation Function.mp4 | 11.3 MB | ||
| 003 Least Squared Error.en.srt | 7.6 KB | ||
| 003 Least Squared Error.mp4 | 26.2 MB | ||
| 003 R-Squared.en.srt | 7.4 KB | ||
| 003 R-Squared.mp4 | 23.1 MB | ||
| 003 Regression 101.en.srt | 4.2 KB | ||
| 003 Regression 101.mp4 | 13.9 MB | ||
| 004 CASE STUDY_ Seasonality with ACF.en.srt | 6 KB | ||
| 004 CASE STUDY_ Seasonality with ACF.mp4 | 31.5 MB | ||
| 004 DOWNLOAD_ Course Resources.html | 1.6 KB | ||
| 004 Machine Learning Part 3 - Regression.pdf | 4.1 MB | ||
| 004 Maven_ML_Demos_Part_3.xlsx | 711.2 KB | ||
| 004 Mean Error Metrics (MSE, MAE, MAPE).en.srt | 8.9 KB | ||
| 004 Mean Error Metrics (MSE, MAE, MAPE).mp4 | 28.1 MB | ||
| 004 Regression Workflow.en.srt | 2 KB | ||
| 004 Regression Workflow.mp4 | 5.7 MB | ||
| 004 Univariate Linear Regression.en.srt | 2.2 KB | ||
| 004 Univariate Linear Regression.mp4 | 3 MB | ||
| 005 CASE STUDY_ Univariate Linear Regression.en.srt | 12.9 KB | ||
| 005 CASE STUDY_ Univariate Linear Regression.mp4 | 43.6 MB | ||
| 005 Feature Engineering.en.srt | 4.2 KB | ||
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| 005 Setting Expectations.en.srt | 4.2 KB | ||
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| 006 CASE STUDY_ Seasonality with One-Hot Encoding.en.srt | 11.9 KB | ||
| 006 CASE STUDY_ Seasonality with One-Hot Encoding.mp4 | 65 MB | ||
| 006 Multiple Linear Regression.en.srt | 8.6 KB | ||
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| 006 Null Hypothesis.en.srt | 1.8 KB | ||
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| 006 Splitting & Overfitting.en.srt | 3.8 KB | ||
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| 007 F-Significance.en.srt | 2.9 KB | ||
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| 007 Linear Trending.en.srt | 3.9 KB | ||
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| 007 Non-Linear Regression.en.srt | 5.2 KB | ||
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| 007 Prediction vs. Root-Cause Analysis.en.srt | 1.8 KB | ||
| 007 Prediction vs. Root-Cause Analysis.mp4 | 5.1 MB | ||
| 008 CASE STUDY_ Non-Linear Regression.en.srt | 12.9 KB | ||
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| 008 CASE STUDY_ Seasonality with Linear Trend.en.srt | 12.9 KB | ||
| 008 CASE STUDY_ Seasonality with Linear Trend.mp4 | 76 MB | ||
| 008 T-Values & P-Values.en.srt | 4.8 KB | ||
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| 009 Multicollinearity.en.srt | 2.4 KB | ||
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| 009 Smoothing.en.srt | 2.7 KB | ||
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| 010 CASE STUDY_ Smoothing.en.srt | 8.2 KB | ||
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| 010 Variance Inflation Factor.en.srt | 5.1 KB | ||
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| 011 Non-Linear Trends.en.srt | 2.1 KB | ||
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| 011 RECAP_ Sample Model Output.en.srt | 6.2 KB | ||
| 011 RECAP_ Sample Model Output.mp4 | 24.6 MB | ||
| 012 CASE STUDY_ Non-Linear Trend.en.srt | 8.9 KB | ||
| 012 CASE STUDY_ Non-Linear Trend.mp4 | 31.1 MB | ||
| 013 Intervention Analysis.en.srt | 4 KB | ||
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| 014 CASE STUDY_ Intervention Analysis.en.srt | 12.2 KB | ||
| 014 CASE STUDY_ Intervention Analysis.mp4 | 76.7 MB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 95 total files | |||
Machine Learning for BI, PART 3: Regression & Forecasting
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 47 lectures (2h 32m) | Size: 774.4 MB
Demystify Machine Learning and build foundational Data Science skills like regression & forecasting, 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
Predict numerical outcomes using regression modeling and time-series forecasting techniques
Calculate diagnostic metrics like R-Squared, Mean Error, F-Significance and P-Values to diagnose model quality
Explore unique, hands-on case studies to see how regression analysis can be applied to real-world business intelligence use cases
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 3 of our Machine Learning for BI series (we recommend taking Parts 1 & 2 first)
Description
This course is PART 3 of a 4-PART SERIES designed to help you build a strong, foundational understanding of Machine Learning:
PART 1: QA & Data Profiling
| torrent name | size | uploader | age | seed | leech |
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Udemy - Spark Machine Learning Project (House Sale Price Prediction) Posted by
freecoursewb in Other
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1.7 GB | freecoursewb | 3 days | 8 | 30 |
| 3.4 GB | freecoursewb | 2 weeks | 19 | 5 | |
| 1.2 GB | freecoursewb | 1 month | 12 | 3 | |
| 1.9 GB | freecoursewb | 1 month | 9 | 1 | |
| 2.6 GB | freecoursewb | 1 month | 5 | 2 |
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