| 1. Algorithm Test Harness - Train-Test-Split-en_US.srt | 5.6 KB | ||
| 1. Algorithm Test Harness - Train-Test-Split.mp4 | 24 MB | ||
| 1. Classification and Regression Trees-en_US.srt | 3.4 KB | ||
| 1. Classification and Regression Trees.mp4 | 8.5 MB | ||
| 1. Introduction-en_US.srt | 2.4 KB | ||
| 1. Introduction.mp4 | 23.1 MB | ||
| 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 KB | ||
| 10. Classification Accuracy.mp4 | 7.7 MB | ||
| 10. Demo Logistic Regression Make Predictions-en_US.srt | 2.1 KB | ||
| 10. Demo Logistic Regression Make Predictions.mp4 | 8.6 MB | ||
| 10. Demo Naïve Bayes Separate by Class-en_US.srt | 3 KB | ||
| 10. Demo Naïve Bayes Separate by Class.mp4 | 9.3 MB | ||
| 11. Confusion Matrix-en_US.srt | 4.5 KB | ||
| 11. Confusion Matrix.mp4 | 12.5 MB | ||
| 11. Demo Logistic Regression Estimating Coefficients-en_US.srt | 3.4 KB | ||
| 11. Demo Logistic Regression Estimating Coefficients.mp4 | 15.2 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 Logistic Regression Diabetes Dataset-en_US.srt | 2.6 KB | ||
| 12. Demo Logistic Regression Diabetes Dataset.mp4 | 11.2 MB | ||
| 12. Demo Naïve Bayes Summarize Data by Class-en_US.srt | 2 KB | ||
| 12. Demo Naïve Bayes Summarize Data by Class.mp4 | 7.7 MB | ||
| 12. Regression Metrics-en_US.srt | 3.6 KB | ||
| 12. Regression Metrics.mp4 | 18 MB | ||
| 13. Baseline Models-en_US.srt | 1.8 KB | ||
| 13. Baseline Models.mp4 | 18.3 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 | ||
| 14. Random Prediction Algorithm-en_US.srt | 2.4 KB | ||
| 14. Random Prediction Algorithm.mp4 | 10.6 MB | ||
| 15. Demo The Perceptron Training Weights-en_US.srt | 2.9 KB | ||
| 15. Demo The Perceptron Training Weights.mp4 | 11.2 MB | ||
| 15. Zero Rule Algorithm-en_US.srt | 4.4 KB | ||
| 15. Zero Rule Algorithm.mp4 | 18.9 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 | ||
| 2. Demo CART Creating the Gini Index-en_US.srt | 3.6 KB | ||
| 2. Demo CART Creating the Gini Index.mp4 | 13.9 MB | ||
| 2. Scale Your Data Normalization-en_US.srt | 3.7 KB | ||
| 2. Scale Your Data Normalization.mp4 | 17.8 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 | ||
| 3. Demo CART Creating the Splits-en_US.srt | 1.8 KB | ||
| 3. Demo CART Creating the Splits.mp4 | 5.4 MB | ||
| 3. Scale Your Data Standardization-en_US.srt | 3.7 KB | ||
| 3. Scale Your Data Standardization.mp4 | 15.4 MB | ||
| 3. Simple Linear Regression-en_US.srt | 3.2 KB | ||
| 3. Simple Linear Regression.mp4 | 11.2 MB | ||
| 4. Algorithm Evaluation Methods-en_US.srt | 1.5 KB | ||
| 4. Algorithm Evaluation Methods.mp4 | 12.4 MB | ||
| 4. Course Structure-en_US.srt | 1.8 KB | ||
| 4. Course Structure.mp4 | 1.8 MB | ||
| 4. Demo CART Evaluating the Splits-en_US.srt | 2.8 KB | ||
| 4. Demo CART Evaluating the Splits.mp4 | 12.4 MB | ||
| 4. Simple Linear Regression Case Study-en_US.srt | 4 KB | ||
| 4. Simple Linear Regression Case Study.mp4 | 12.9 MB | ||
| 5. CART Building the Tree-en_US.srt | 3.1 KB | ||
| 5. CART Building the Tree.mp4 | 5.9 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 | ||
| 5. Train-Test Split-en_US.srt | 3.8 KB | ||
| 5. Train-Test Split.mp4 | 12.7 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 | ||
| 6. Demo CART Recursive Splitting-en_US.srt | 2.7 KB | ||
| 6. Demo CART Recursive Splitting.mp4 | 9.6 MB | ||
| 6. K-Fold Cross-Validation Defined-en_US.srt | 2.7 KB | ||
| 6. K-Fold Cross-Validation Defined.mp4 | 2.7 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 CART Assembling the Tree-en_US.srt | 2.3 KB | ||
| 7. Demo CART Assembling the Tree.mp4 | 8.7 MB | ||
| 7. Demo Multivariate Linear Regression Case Study-en_US.srt | 5.4 KB | ||
| 7. Demo Multivariate Linear Regression Case Study.mp4 | 20 MB | ||
| 7. K-Fold Cross-Validation-en_US.srt | 2.6 KB | ||
| 7. K-Fold Cross-Validation.mp4 | 7 MB | ||
| 8. Choosing a Resampling Method-en_US.srt | 1.9 KB | ||
| 8. Choosing a Resampling Method.mp4 | 17.4 MB | ||
| 8. Demo CART CART to Banknote Dataset-en_US.srt | 2.4 KB | ||
| 8. Demo CART CART to Banknote Dataset.mp4 | 8.7 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. Evaluation Metrics-en_US.srt | 2 KB | ||
| 9. Evaluation Metrics.mp4 | 18.5 MB | ||
| 9. Logistic Regression Defined-en_US.srt | 4 KB | ||
| 9. Logistic Regression Defined.mp4 | 19.4 MB | ||
| 9. Naïve Bayes-en_US.srt | 1.8 KB | ||
| 9. Naïve Bayes.mp4 | 5.9 MB | ||
| Bonus Resources.txt | 307.2 B | ||
| Building Machine Learning Algorithms in Python - Algorithm Test Harness.ipynb | 6.3 KB | ||
| 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 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 | ||
| Building Machine Learning Algorithms in Python - Simple Linear Regression.ipynb | 8.9 KB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| contrived-dataset.xlsx | 36.7 KB | ||
| insurance.csv | 512 B | ||
| ▲ 107 total files | |||
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.
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