Machine-Learning-Algorithms

About

The purpose of this repository is self-motivation and to keep track of my Machine learning, Natural Language Processing & Data Science related stuff progress

Table of contents

• Articles
• Kaggle competition
• Projects
• Programming Showcase
  • Supervised Machine Learning
  • Unupervised Machine Learning
  • Learning Curve Analysis
  • Time Series Analysis
• Deep Learning
• NLP
• Machine Learning-Stanford-Andrew Ng-exercises
• Courses & Certificates
• Contact

Table of contents generated with markdown-toc

Articles

Title	Link	Publsh Date
Probabilistic Justification for specific Loss function in Machine Learning Algorithms	Medium	4.02.2021
Hybrid Recommendation System Web-Application Part 1: Exploratory Data Analysis with PostgreSQL	Medium	3.04.2021

Kaggle competition

Title	Description	Link
Natural Language Processing with Disaster Tweets	Natural Language Processing	Link
Kaggle California Housing Prices Analysis And prediction	Linear Regression,DecisionTreeRegressor,RandomForestRegressor	Link
kc house data price nd prediction	Ensemble Learning Boosting(XGboost) and Ensemble Learning Bagging(RandomForestRegressor)	Link

Projects

Title	Description	Link
Movie Recommender System	Implemented a Regression-based Hybrid of Collaborative Filtering and Content-Based Recommendation System from scratch for a Movie Recommendation web-Application and deployed it using Flask.	Link
Myers–Briggs Type Indicator (MBTI) classification Web Application	Implemented Recurrent Neural Networks(RNN) with LSTM and Multinomial Logistic Regression using Bag of words and TF-IDF features in Flask web app to classify, “Myers–Briggs Type Indicator (MBTI)” personality types, Collected data using Pushshift API from Reddit performed data cleaning, analysis, and exploration, used SMOTE to solve class imbalance problem	Link
Consumer-Finance-Complaints-Text-classification-with-PostgreSQL	Classifying Consumer Finance Complaints into one of eleven product categories, The problem is a Text classification, also known as text tagging or text categorization. Text classifiers can automatically analyze text and then assign a set of pre-defined tags or categories based on its content. In this problem, I have taken 'consumer_complaint_narrative' as “text” and to classify each consumer_complaint_narrative / “text” into one of eleven pre-defined categories of product.	Link
Politician Face Classifier	collected images from google through web-scraping performed data cleaning, data preprocessing, exploratory data analysis, and build machine learning models such as Logistic Regression, Random Forest, and SVM(Support vector machine) achieved 98% test accuracy and deployed model to production, Used Numpy, OpenCV, SKlearn, CSS, Html, Flask, JavaScript, Selenium	Link
Handwritten Digit Recognizer	Neural Network from scratch in Python to recognize handwritten digit achieved 98.45% test accuracy and using Keras CNN(Convolutional neural network) achieved 99.25% test accuracy deployed model to production	Link

Programming Showcase

Supervised Machine Learning

Algorithm	Description	Implementation
Univariate Linear Regression	Univariate Linear Regression from scratch using Pyhton	Link
Multivariate Linear Regression	Multivariate Linear Regression from scrach using Pyhton	Link
Locally Weighted Regression	Locally Weighted Regression from scrach using Pyhton	Link
Normal Equation closed-form solution	Normal Equation closed-form solution from scratch	Link
Ridge Polynomail Regression with Learnin Curve Analysis	Regularized Polynomail Regression with Learnin Curve Analysis from scrach	Link
Logistic Regression on Linear Data	Logistic Regression on Linear Data from scratch	Link
Logistic Regression on Nonlinear Data	Logistic Regression on Nonlinear Data from scratch	Link
One-vs-all Classification Using Logistic Regression	One-vs-all Classification Using Logistic Regression from scratch	Link
Support vector machine using LinearKernel and gaussianKernel	Support vector machine using LinearKernel and gaussianKernel from scratch	Link
Ensemble Learning Bagging(RandomForestRegressor)	SKlearn	Link
Ensemble Learning Boosting(XGboost)	SKlearn	Link

Unupervised Machine Learning

Algorithm	Description	Implementation
K-MEANS	K-MEANS from scratch using Python	Link
Anomaly Detection	Anomaly Detection from scratch using Python	Link
PCA	PCA from scratch using Python	Link

Learning Curve Analysis

Algorithm	Description	Implementation
Learning Curve Analysis of Regularized Linear and Polynomail Regression	-	Link
Learning Curves Using Scikit-learn	-	Link

Time Series Analysis

Algorithm	Description	Implementation
Time Series Analysis	Time Series Analysis using Python	Link

Deep Learning

Algorithm	Description	Implementation
DNN	DNN implmentation from scratch	Link
CNN	CNN using Keras	Link
LSTM RNN	LSTM RNN implmentation uing keras	Link

NLP

Algorithm	Description	Implementation
Word Embedding,TF-IDF,BOW with Embedding and LSTM Recurrent Neural Network	Word Embedding,TF-IDF,BOW with Embedding and LSTM Recurrent Neural Network on kaggle dataset	Link

Machine Learning-Stanford-Andrew Ng-exercises

Title	Description	Link
ML exercises	Octave/MATHLAB	Link

Courses & Certificates

• Introduction to Database Systems (2019) edX-uc berkeley university-Joe Hellerstein
• Introduction to Computer Science and Programming Using Python(2017) edX-MIT-Eric Grimson
• CS 61B: Data Structures(2018) OCW-uc berkeley university-Jonathan Shewchuk
• Mathematics for Computer Science/Discrete mathematics OCW- MIT-Prof. Tom Leighton
• Linear Algebra(2019) OCW-MIT-Prof. Gilbert Strang
• Single Variable Calculus(2020) OCW-MIT-Prof. David Jerison
• Introduction to Probability (2020)OCW-MIT-Prof. John Tsitsiklis
• Introduction to Compiler Construction(2019)OCW-The Paul G. Allen Center for Computer Science & Engineering-Hal Perkins
• Artificial Intelligence(2018) edX-uc berkeley university-Dan Klein
• Machine Learning-Stanford(2020) Coursera - Stanford - Andrew Ng
• CS229 - Machine Learning(2020)Stanford - Andrew Ng
• Introduction to Algorithms(2018)OCW-MIT-Srini Devadas & Erik Demaine

Contact

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