It contains an Artificial Neural Net implemented from scratch in Python.

Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases.It is intended to identify strong rules discovered in databases using some measures of interestingness.The folder contains a .py file illustrating the algorithm.

Decision tree learning uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It contains the following implementations 1.Rain Predictor Decision Tree 2.Basic Decision Tree 3.Decision Tree visualization of Iris dataset

KMeans is an unsupervised Machine Learning Algorithm, which is implemented from scratch here. Iris dataset is used for testing and training

K- Nearest Neighbor Classification Algorithm is widely used for classification problem. Here, the algorithm is implemented from scratch and tested first on a small example of 2 dimensional test data and then on iris dataset.

In Linear Regression, we try to establish relationship between 2 variables by fitting a line. The best fit line is known as regression line and it is represented by a linear equation Y = a*X +b This folder contains Linear Regression implemented from scratch in Python without using any Machine Learning libraries.

Logistic Regression is basically a classification algorithm which is used to estimate discrete values based on given data. The folder contains an ipython notebook depicting the implementation of logistic regression without any helper machine learning libraries.

Naive Bayes is a popular classification algorithm which assumes that one variable is independent of the other. In this example, the ipython notebook implements an email spam classifier.

A recommender system or a recommendation system (sometimes replacing "system" with a synonym such as platform or engine) is a subclass of information filtering system that seeks to predict the "rating" or "preference" that a user would give to an item.Contains an .ipynb file explaining the algorithm.

This folder includes the following

1. An ipython notebook depicting basic implementation of SVM algorithm for learning basis. https://github.com/aribis369/ML-Starter-Pack/blob/master/SVM/SVM.ipynb

2. Optimized SVM using the SMO Algorithm https://github.com/aribis369/ML-Starter-Pack/blob/master/SVM/SVMl.ipynb

3. A pdf for explanation of SMO Algorithm https://github.com/aribis369/ML-Starter-Pack/blob/master/SVM/smo.pdf

The folder also contains .py files of the same and some datasets for illustrating the algorithms.

The folder contains .py file implenting the algorithms.

Anomaly detection is a technique used to identify unusual patterns that do not conform to expected behavior, called outliers. It is used to detect intrusions in system, fraud detection and so on. The folder contains a python file which implements an anomaly detector from scratch.

Collaborative Filtering Algorithm is used in Recommender System. In collaborative filtering, the recommender system recommends items based on how users with similar interests liked it. The python file gives a collaborative filtering algorithm for recommending items to different users based on ratings and trust of reviews.

PCA (Principal Component Analysis) algorithm is used for reducing dimensions. For example, If we want to reduce our data from 3D to 2D, we can use PCA algorithm. An ipython notebook as well as a python script is added in the folder which shows the implementation of this algorithm.

1. Digit Recognition in MNIST dataset using CNN
2. Iceberg Recognition from Satellite images using Keras Deep NN.
3. Basic Neural Network using Keras.
4. Hand Sign Recognization system using Residual Networks(ResNets)

1. Classification of biomechanical orthopedic patients using Pytorch tensor
2. Implementing a neural network using pyTorch
3. Hand Sign Image Recognition using Pytorch CNN.
4. Hand Written Digit Recognition using Pytorch Deep NN.

1. Analyzing Iris Dataset using Linear Regression, Logistic Regression, Decision Tree, Naive Bayes and SVM.
2. Analysis of Titanic dataset
3. Using Bernoullis Naive Bayes to classify a mushroom dataset and predict whether it is edible or not.

1. Digit classification from MNIST dataset using CNN
2. Digit classification from MNIST dataset using ANN
3. Simple Reccurent Neural Network using Tensorflow.

1. Implementation of gradient descent(an optimizing algorithm) from start.
2. Implementation of MiniBatch Gradient Descent Algorithm

Recurrent Neural Networks allow information to persist, solving one major drawback of traditional neural nets. LSTMs are designed specially to avoid the long-term dependency problem. Remembering information for long periods of time is fortunately their default behavior, and not something they struggle to learn. A stock preditor using LSTM is implemented here.

Understanding and Implementation of Decision Tree from Scratch with different types of examples. Examples like Rain Predictor Decision Tree and Effective ways to visualize data . Go to the folder and Enjoy Learnig :

• https://github.com/Lightning-Bug/ML-Starter-Pack/tree/master/Decision%20Tree%20Classifier

Ever wondered, “what algorithm Google uses to maximize its target ads revenue?”. What about the Giant e-commerce websites like Amazon which advocates you through options such as ‘people who bought this also bought this’? The answer is Recommendation Engines. Go to the folder and Explore them from Scratch :

• https://github.com/Lightning-Bug/ML-Starter-Pack/tree/master/Recommedation%20Engines