Building Machine Learning Algorithms in Python 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.

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Course curriculum

1. Course Introduction
2. What is this Course Exactly?
3. Course Outcomes
4. Course Structure
5. What is an Algorithm?
1. Loading a CSV File
1. Scale Your Data: Normalization
2. Scale Your Data: Standardization
1. Establishing a Baseline
2. Random Prediction Algorithm
3. Zero Rule Algorithm
1. Algorithm Evaluation Methods
2. Train-Test Split
3. K-Fold Cross-Validation
4. How to Choose a Resampling Algorithm?
1. Evaluation Metrics
2. Classification Accuracy
3. Confusion Matrix
4. Regression Metrics
5. K-Fold Cross-Validation Defined
1. Algorithm Test Harness - Train-Test-Split
2. Algorithm Test Harness - K-Fold
3. Simple Linear Regression
4. Simple Linear Regression Case Study
5. Simple Linear Regression Case Study: Part 2
6. Multivariate Linear Regression Case Study
7. Demo: Multivariate Linear Regression Case Study
8. Demo: Linear Regression on Wine Quality Dataset
9. Logistic Regression Defined
10. Demo: Logistic Regression: Make Predictions
11. Demo: Logistic Regression : Estimating Coefficients
12. Demo: Logistic Regression : Diabetes Dataset
13. The Perceptron
14. Demo: The Perceptron: Make Predictions
15. Demo: The Perceptron: Training Weights
16. Demo: The Perceptron: Sonar Dataset
1. Classification and Regression Trees
2. Demo: CART : Creating the Gini Index
3. Demo: CART : Creating the Splits
4. Demo: CART : Evaluating the Splits
5. CART : Building the Tree
6. Demo: CART : Recursive Splitting
7. Demo: CART : Assembling the Tree
8. Demo: CART : CART to Banknote Dataset
9. Naïve Bayes
10. Demo: Naïve Bayes: Separate by Class
11. Demo: Naïve Bayes: Summarize the Dataset
12. Demo: Naïve Bayes: Summarize Data by Class
13. Demo: Naïve Bayes: Gaussian Probability Density Function
14. Demo: Naïve Bayes: Class Probabilities
15. Demo: Naïve Bayes: Iris Flowers Dataset
16. K-Nearest Neighbors
17. Demo: KNN: Calculate Euclidean Distance
18. Demo: KNN: Get Nearest Neighbors
19. Demo: KNN: Making Predictions
20. Demo: KNN: Iris Dataset
21. Artificial Neural Network
22. Demo: Artificial Neural Network: Initialize Network
23. Demo: Artificial Neural Network: Forward Propagation
24. Demo: Artificial Neural Network: Error Backpropagation
25. Demo: Artificial Neural Network: Training the Network
26. Demo: Artificial Neural Network: Predictions
27. Demo: Artificial Neural Network: Final Model on Real Dataset

About this course

Free
63 lessons
2 hours of video content

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Building Machine Learning Algorithms in Python from Scratch

Course curriculum

Introduction

Data Preparation

Scale Data

Baseline Models

Algorithm Evaluation Methods

Evaluation Metrics

Linear Algorithms

Nonlinear Algorithms

About this course