Machine Learning in Python From scratch
A Vectorized Python
This course helped me write a blog answering the following question What is Machine Learning?
Supervised Learning
Given a set of labeled observations, find a function f which can be used to assign a class or value to unseen observations. Predictions should be similar to real labels.
Regression
In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function.
1. Linear regression with one variable to predict profits for a food truck
2. Regularized Linear regression with multiple variables to predict the prices of houses
🐍 Demo | Linear Regression with multiple variables Notebook▶️ Demo | Linear Regression with multiple variables Matlab
Classification
In a classification problem, we instead are trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.
3. Regularized logistic regression to predict whether microchips passes quality assurance (QA)
4. Multi-class Logistic regression to recognize handwritten digits
5. Neural Networks (MLP) to recognize handwritten digits
🐍 Demo | Neural Networks Notebook Part I, Demo | Neural Networks Notebook Part II▶️ Demo | Neural Networks Matlab Part I, Demo | Neural NetworksMatlab Part II
6. Support Vector Machines SVM ( with and without Gaussian Kernels)
Metrics to evaluate ML algorithms
Tackling Overfitting and Underfitting problems.
7. High Bias vs High Variance
Unsupervised Learning
Labeling can be tedious (too long, too slow), often done by humans and no real labels to compare. Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables. We can derive this structure by clustering the data based on relationships among the variables in the data. With unsupervised learning there is no feedback based on the prediction results.
Clustering
Group objects in clusters, similar within cluster, dissimilar between clusters.
8. K-means clustering algorithm for image compression
Dimensionality reduction
Reduce data set dimensions. Used for ata compression or big data visualization.
9. Principal Component Analysis (PCA) to perform dimensionality reduction
Anomaly detection
Identifies rare items (outliers) which raise suspicions by differing significantly from the majority of the data.
10. Anomaly detection algorithm to detect anomalous behavior in server computers of a data center
Recommender System
Predicts the rating or preference a user would give to an item.
11. Collaborative filtering recommender system applied to a dataset of movie ratings
🐍 Demo | Collaborative filtering recommender system Notebook▶️ Demo | Collaborative filtering recommender system Matlab
