Data-science-end-to-end

This repository is a collection of all the algorithms and techniques being which have been developed and implemented since the advent of Machine Learning era. I would like to thank Coding Blocks for it's wonderful Data Science course which has helped to construct, add and modify many parts of this repository. I hope whoever is looking for implementation codes for various ML/AI techniques will find this repository helpful and enlightening. Some components of the repository are incomplete and future updates will aim towards a more wholesome and completed form of this collection.

Topics covered:

• Basics of Python
• Data acquisition and web scraping
• Understanding various ML libraries, including Pandas, Numpy, Matplotlib, Seaborn and so on.
• Learning about probability distribution and data visualization, alongside linear algebra
• K-nearest neighbour algorithm
• openCV and Image processing
• Linear regression, LOWESS, MLE and Logistic Regression
• PCA, NLP, NLTK and feature selection
• Gaussian Naive bayes, Multinomial Naive Bayes and Multivariate Bernoulli
• Decision trees, random forests and support vector machine
• Clustering using K means approach
• Neural Networks, Multi-Layer Perceptrons
• CNN, RNN and LSTMs : architecture and implementation using Keras
• GANs and DCGANs
• Projects related to various topics
• Brief project implementation of Flask, Django and related frameworks with ML

References:

• Coding Blocks