The purpose of this repository is self-motivation and to keep track of my Machine learning, Natural Language Processing & Data Science related stuff progress
- Articles
- Kaggle competition
- Projects
- Programming Showcase
- Deep Learning
- NLP
- Machine Learning-Stanford-Andrew Ng-exercises
- Courses & Certificates
- Contact
Table of contents generated with markdown-toc
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| Algorithm | Description | Implementation |
|---|---|---|
| Learning Curve Analysis of Regularized Linear and Polynomail Regression | - | Link |
| Learning Curves Using Scikit-learn | - | Link |
| Algorithm | Description | Implementation |
|---|---|---|
| Time Series Analysis | Time Series Analysis using Python | Link |
| Algorithm | Description | Implementation |
|---|---|---|
| DNN | DNN implmentation from scratch | Link |
| CNN | CNN using Keras | Link |
| LSTM RNN | LSTM RNN implmentation uing keras | Link |
| 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 |
| Title | Description | Link |
|---|---|---|
| ML exercises | Octave/MATHLAB | Link |
- 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