Authoring Machine Learning Models from Scratch

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size611.7 MB
  • Uploaded Byfreecoursewb
  • Downloads69
  • Last checkedAug. 15th '21
  • Date uploadedAug. 12th '21
  • Seeders 5
  • Leechers9

Infohash : 389E8C8AD6B097B767694CF6E62632AC7976A43F

Authoring Machine Learning Models from Scratch



MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 611 MB | Duration: 1h 29m
What you'll learn
You'll learn how to author machine learning models in Python without the aide of frameworks or libraries.
You'll learn to code the functions of the most commonly used tools in machine learning.
You'll gain insight into who real-world machine learning models are written.
You will gain a deep appreciation for how the algorithm works

Requirements
You'll need to have a solid background in Python.
You'll need to have a solid background in machine learning.
Description
Welcome to Authoring Machine Learning Models from Scratch

This is your guide to learning the details of machine learning algorithms by implementing them from scratch in Python. You will discover how to load data, evaluate models and implement a suite of top machine learning algorithms using step-by-step tutorials.

Machine learning algorithms do have a lot of math and theory under the covers, but you do not need to know why algorithms work to be able to implement them and apply them to achieve real and valuable results. Most developers that I know (myself included) learn best by implementing. It is our preferred learning style and it is the reason that I created this course.

Files:

[ FreeCourseWeb.com ] Udemy - Authoring Machine Learning Models from Scratch
  • Get Bonus Downloads Here.url (0.2 KB)
  • ~Get Your Files Here ! 1. Introduction
    • 1. Introduction-en_US.srt (2.4 KB)
    • 1. Introduction.mp4 (23.1 MB)
    • 2. What is this course... exactly-en_US.srt (1.8 KB)
    • 2. What is this course... exactly.mp4 (1.8 MB)
    • 3. Course Outcomes-en_US.srt (2.2 KB)
    • 3. Course Outcomes.mp4 (10.2 MB)
    • 4. Course Structure-en_US.srt (1.8 KB)
    • 4. Course Structure.mp4 (1.8 MB)
    • 5. What is an Algorithm in Programming-en_US.srt (4.1 KB)
    • 5. What is an Algorithm in Programming.mp4 (25.4 MB)
    2. Data Preparation
    • 1. Loading Data from a CSV File-en_US.srt (6.1 KB)
    • 1. Loading Data from a CSV File.mp4 (28.1 MB)
    • 10. Classification Accuracy-en_US.srt (2.0 KB)
    • 10. Classification Accuracy.mp4 (7.7 MB)
    • 11. Confusion Matrix-en_US.srt (4.5 KB)
    • 11. Confusion Matrix.mp4 (12.5 MB)
    • 12. Regression Metrics-en_US.srt (3.6 KB)
    • 12. Regression Metrics.mp4 (18.0 MB)
    • 13. Baseline Models-en_US.srt (1.8 KB)
    • 13. Baseline Models.mp4 (18.3 MB)
    • 14. Random Prediction Algorithm-en_US.srt (2.4 KB)
    • 14. Random Prediction Algorithm.mp4 (10.6 MB)
    • 15. Zero Rule Algorithm-en_US.srt (4.4 KB)
    • 15. Zero Rule Algorithm.mp4 (18.9 MB)
    • 2. Scale Your Data Normalization-en_US.srt (3.7 KB)
    • 2. Scale Your Data Normalization.mp4 (17.8 MB)
    • 3. Scale Your Data Standardization-en_US.srt (3.7 KB)
    • 3. Scale Your Data Standardization.mp4 (15.4 MB)
    • 4. Algorithm Evaluation Methods-en_US.srt (1.5 KB)
    • 4. Algorithm Evaluation Methods.mp4 (12.4 MB)
    • 5. Train-Test Split-en_US.srt (3.8 KB)
    • 5. Train-Test Split.mp4 (12.7 MB)
    • 6. K-Fold Cross-Validation Defined-en_US.srt (2.7 KB)
    • 6. K-Fold Cross-Validation Defined.mp4 (2.7 MB)
    • 7. K-Fold Cross-Validation-en_US.srt (2.6 KB)
    • 7. K-Fold Cross-Validation.mp4 (7.0 MB)
    • 8. Choosing a Resampling Method-en_US.srt (1.9 KB)
    • 8. Choosing a Resampling Method.mp4 (17.4 MB)
    • 9. Evaluation Metrics-en_US.srt (2.0 KB)
    • 9. Evaluation Metrics.mp4 (18.5 MB)
    • Building Machine Learning Algorithms in Python - Baseline Algorithms.ipynb (2.9 KB)
    • Building Machine Learning Algorithms in Python - Evaluation Metrics.ipynb (4.7 KB)
    • Building Machine Learning Algorithms in Python - Loading Data.ipynb (5.0 KB)
    • Building Machine Learning Algorithms in Python - Scaling Data with Normalization.ipynb (4.4 KB)
    • Building Machine Learning Algorithms in Python - Scaling Data with Standardization.ipynb (4.7 KB)
    3. Linear Algorithms
    • 1. Algorithm Test Harness - Train-Test-Split-en_US.srt (5.6 KB)
    • 1. Algorithm Test Harness - Train-Test-Split.mp4 (24.0 MB)
    • 10. Demo Logistic Regression Make Predictions-en_US.srt (2.1 KB)
    • 10. Demo Logistic Regression Make Predictions.mp4 (8.6 MB)
    • 11. Demo Logistic Regression Estimating Coefficients-en_US.srt (3.4 KB)
    • 11. Demo Logistic Regression Estimating Coefficients.mp4 (15.2 MB)
    • 12. Demo Logistic Regression Diabetes Dataset-en_US.srt (2.6 KB)
    • 12. Demo Logistic Regression Diabetes Dataset.mp4 (11.2 MB)
    • 13. The Perceptron-en_US.srt (2.2 KB)
    • 13. The Perceptron.mp4 (8.6 MB)
    • 14. Demo The Perceptron Make Predictions-en_US.srt (3.1 KB)
    • 14. Demo The Perceptron Make Predictions.mp4 (16.4 MB)
    • 15. Demo The Perceptron Training Weights-en_US.srt (2.9 KB)
    • 15. Demo The Perceptron Training Weights.mp4 (11.2 MB)
    • 16. Demo The Perceptron Sonar Dataset-en_US.srt (2.5 KB)
    • 16. Demo The Perceptron Sonar Dataset.mp4 (10.5 MB)
    • 2. Algorithm Test Harness - K-Fold-en_US.srt (3.3 KB)
    • 2. Algorithm Test Harness - K-Fold.mp4 (14.8 MB)
    • 3. Simple Linear Regression-en_US.srt (3.2 KB)
    • 3. Simple Linear Regression.mp4 (11.2 MB)
    • 4. Simple Linear Regression Case Study-en_US.srt (4.0 KB)
    • 4. Simple Linear Regression Case Study.mp4 (12.9 MB)
    • 5. Simple Linear Regression Case Study Part 2-en_US.srt (3.2 KB)
    • 5. Simple Linear Regression Case Study Part 2.mp4 (11.3 MB)
    • 6. Multivariate Linear Regression Case Study-en_US.srt (2.4 KB)
    • 6. Multivariate Linear Regression Case Study.mp4 (12.4 MB)
    • 7. Demo Multivariate Linear Regression Case Study-en_US.srt (5.4 KB)
    • 7. Demo Multivariate Linear Regression Case Study.mp4 (20.0 MB)
    • 8. Demo Linear Regression on Wine Quality Dataset-en_US.srt (2.9 KB)
    • 8. Demo Linear Regression on Wine Quality Dataset.mp4 (12.6 MB)
    • 9. Logistic Regression Defined-en_US.srt (4.0 KB)
    • 9. Logistic Regression Defined.mp4 (19.4 MB)
    • Building Machine Learning Algorithms in Python - Algorithm Test Harness.ipynb (6.3 KB)
    • Building Machine Learning Algorithms in Python - Simple Linear Regression.ipynb (8.9 KB)
    • contrived-dataset.xlsx (36.7 KB)
    • insurance.csv (0.5 KB)
    4. Non-Linear Regression
    • 1. Classification and Regression Trees-en_US.srt (3.4 KB)
    • 1. Classification and Regression Trees.mp4 (8.5 MB)
    • 10. Demo Naïve Bayes Separate by Class-en_US.srt (3.0 KB)
    • 10. Demo Naïve Bayes Separate by Class.mp4 (9.3 MB)
    • 11. Demo Naïve Bayes Summarize the Dataset-en_US.srt (3.9 KB)
    • 11. Demo Naïve Bayes Summarize the Dataset.mp4 (15.2 MB)
    • 12. Demo Naïve Bayes Summarize Data by Class-en_US.srt (2.0 KB)
    • 12. Demo Naïve Bayes Summarize Data by Class.mp4 (7.7 MB)
    • 2. Demo CART Creating the Gini Index-en_US.srt (3.6 KB)
    • 2. Demo CART Creating the Gini Index.mp4 (13.9 MB)
    • 3. Demo CART Creating the Splits-en_US.srt (1.8 KB)
    • 3. Demo CART Creating the Splits.mp4 (5.4 MB)
    • 4. Demo CART Evaluating the Splits-en_US.srt (2.8 KB)
    • 4. Demo CART Evaluating the Splits.mp4 (12.4 MB)
    • 5. CART Building the Tree-en_US.srt (3.1 KB)
    • 5. CART Building the Tree.mp4 (5.9 MB)
    • 6. Demo CART Recursive Splitting-en_US.srt (2.7 KB)
    • 6. Demo CART Recursive Splitting.mp4 (9.6 MB)
    • 7. Demo CART Assembling

Code:

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