solee7650 / Machine-Learning-Notebooks

Machine Learning notebooks for refreshing concepts.

Machine Learning Notebooks

Helpful jupyter noteboks that I compiled while learning Machine Learning and Deep Learning from various sources on the Internet.

NumPy Basics:

1. NumPy Basics

Data Preprocessing:

1. Feature Selection: Imputing missing values, Encoding, Binarizing.

2. Feature Scaling: Min-Max Scaling, Normalizing, Standardizing.

3. Feature Extraction: CountVectorizer, DictVectorizer, TfidfVectorizer.

Regression

1. Linear & Multiple Regression

   • a. Theory and Derivation

   • b. Linear Regression from scratch

   • c. Assumptions in Linear Regression: Assumptions in Linear Regression, Dummy Variable Trap

   • d. Linear Regression using Scikit-learn: Simple and Multivariable Regression using Scikit-learn.

2. Backward Elimination: Method of Backward Elimination, P-values.

3. Polynomial Regression

4. Support Vector Regression

5. Decision Tree Regression

6. Random Forest Regression

7. Robust Regression using Theil-Sen Regression

8. Pipelines in Scikit-Learn

Classification

1. Logistic Regression

   • a. Logistic Regression and Gradient Descent

   • b. Deriving Logistic Regression

   • c. Logistic Regression using Gradient Descent

   • d. Logistic Regression using Sci-kit learn

2. Regularization

   • a. Regularization

   • b. Regularization on Logistic Regression

3. K Nearest Neighbors

4. Support Vector Machines

5. Naive Bayes

6. Decision Trees

Clustering

1. KMeans

2. Minibatch KMeans

3. Hierarchical Clustering

4. Application of Clustering - Image Quantization

5. Application of Custering - Outlier Detection

Model Evalutaion

1. Cross Validation and its types

2. Confusion Matrix, Precision, Recall

3. R Squared

4. ROC Curve, AUC

5. Silhoutte Distance

Associate Rule Mining

1. Apriori Algorithm

2. Eclat Model

Reinforcement Learning

1. Upper Confidence Bound Algorithm

2. Thompson Sampling

Natural Language Processing

1. Sentiment Analysis

Neural Networks

1. What are Activation Functions

2. Vanilla Neural Network

3. Backpropagation Derivation

4. Backpropagation in Python

5. Convolutional Neural Networks

6. Long Short Term Memory Neural Networks (LSTM)

Sources / References:

1. Machine Learning by Andrew Ng (Coursera)
2. Machine Learning A-Z (Udemy)
3. Deep Learning A-Z (Udemy)
4. Neural Networks by Geoffrey (Hinton Coursera)
5. Scikit-learn Cookbook (Second Edition) - Julian Avila et. al