Authoring Machine Learning Models from Scratch [Video]
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Free ChapterIntroduction
-
Data Preparation
- Loading Data from a CSV File
- Scale Your Data: Normalization
- Scale Your Data: Standardization
- Algorithm Evaluation Methods
- Train-Test Split
- K-Fold Cross-Validation Defined
- K-Fold Cross-Validation
- Choosing a Resampling Method
- Evaluation Metrics
- Classification Accuracy
- Confusion Matrix
- Regression Metrics
- Baseline Models
- Random Prediction Algorithm
- Zero Rule Algorithm
-
Linear Algorithms
- Algorithm Test Harness - Train-Test-Split
- Algorithm Test Harness - K-Fold
- Simple Linear Regression
- Simple Linear Regression Case Study: Part 1
- Simple Linear Regression Case Study: Part 2
- Multivariate Linear Regression Case Study
- Demo: Multivariate Linear Regression Case Study
- Demo: Linear Regression on Wine Quality Dataset
- Logistic Regression Defined
- Demo: Logistic Regression: Make Predictions
- Demo: Logistic Regression: Estimating Coefficients
- Demo: Logistic Regression: Diabetes Dataset
- Perceptron
- Demo: Perceptron: Make Predictions
- Demo: Perceptron: Training Weights
- Demo: Perceptron: Sonar Dataset
-
Non-Linear Regression
- Classification and Regression Trees
- Demo: CART: Creating the Gini Index
- Demo: CART: Creating the Splits
- Demo: CART: Evaluating the Splits
- CART: Building the Tree
- Demo: CART: Recursive Splitting
- Demo: CART: Assembling the Tree
- Demo: CART: CART to Banknote Dataset
- Naïve Bayes
- Demo: Naïve Bayes: Separate by Class
- Demo: Naïve Bayes: Summarize the Dataset
- Demo: Naïve Bayes: Summarize Data by Class
About this
video
A complete 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.
In this course, you will learn how to load from CSV files and prepare data for modeling; how to select algorithm evaluation metrics and resampling techniques for a test harness; how to develop a baseline expectation of performance for a given problem; how to implement and apply a suite of linear machine learning algorithms; how to implement and apply a suite of advanced nonlinear machine learning algorithms; how to implement and apply ensemble machine learning algorithms to improve performance.
This course will be an invaluable guide to understanding real-world machine learning models and help you understand the code behind math.
By the end of this course, you will gain insight into real-world machine learning models and learn how to code the functions of the most used tools in machine learning.
The complete code bundle for this course is available at https://github.com/PacktPublishing/Authoring-Machine-Learning-Models-from-Scratch
- Publication date:
- November 2021
- Publisher
- Packt
- Duration
- 1 hours 31 minutes
- ISBN
- 9781803238272
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