Disclaimer: Basic Python and Mathematics is a pre-requisite.
Machine Learning is all about writing model that enables machines to learn from data, using mathematics (statistics, linear algebra, probability and calculus) and a programming language.
We will write python codes in jupyter notebook that makes it easy to visualize and explain. Download and Install: Anaconda.
I will try and explain things as we implement it.
- Linear Regression (code) (dataset)
-
Logistic Regression
(code)
(dataset)
Logistic Regression (using Library) (code) (dataset) -
Neural Network
(code)
Neural Network (TensorFlow) (code)
Deep Neural Network [Binary Classification] (Keras) (code)
Deep Neural Network [Multi-class Classification] (Keras) (code)
Convolution Neural Network (Keras) (code) -
Unsupervised Learning (K-Means Clustering)
(code)
Unsupervised Learning (DBSCAN Clustering) (code) - Support Vector Machine (Classification and Regression) (code)
- Principal Component Analysis (code)
- Anomaly Detection (code)
On a final note, let me know if you desire modification / found bug / have doubts at chettri@live.com.