Master Algorithmic Programming Techniques. Learn algorithms through programming and advance your software engineering or data science career

Learning Based Java (LBJava)

Simple Tensorflow tutorials for learning by example

Use Tensorflow and NumPy to implement these basic ML models and learning algorithms from Coursera ML course

Contains machine learning algorithms I have implemented from scratch. Also contain ancilliary functions.

A repo holding the implementation as well as some theoretical explanation of the important relevant concepts. It is going to be in development for a long long time. I'll keep adding things everytime I have something to add to it, and I have the time for it. One can use it to learn the basics of Machine Learning from kind of scratch.

Open Deep Learning and Reinforcement Learning lectures from top Universities like Stanford University, MIT, UC Berkeley. And an intent classifier which can classifies a query into one of the 21 given intents.

Library for Lagrange Interpolation

Lottery analysis + features for use in Machine Learning algorithms

Fully functional implementations of the most popularly used neural-network algorithms.

🧠 💡 🤖 Projects based in Machine Learning Labs. This projects was built using Jupyter Notebook, iPython, Python, Anaconda, Spyder IDE and JetBrains PyCharm. This repository it's based in some practical lab exercises and examples related with Machine Learning, using some libraries like NumPy, Matplotlib, SciPy, Pandas and TensorFlow to work on some datasets, among many others!

Sample programs showing solutions to puzzles solved using Greedy Algorithm

Playground for some learning algorithms on CPU & GPU

A fast and easy to use decision tree learner in java

This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice. No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs. Sorry, we do not believe in multiple choice questions when it comes to learning algorithms...or anything else in computer science! For each algorithm you develop and implement, we designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming. The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.

Predict the star rating of a movie rating based on its content

ML Algorithms implimented in Python

Collection of Learning Algorithms.

Determine taste of chemical molecules from their structure

Implementation of Machine Learning, Deep Learning, Reinforcement Learning, Statistics, and Optimization algorithms in Python. I have organized the codes in respective sub-folders. Ongoing work!

The objective of this project is to implement the entire mechanism, including gradient descent and linear regression, of the various learning algorithms. [Project done on Coursera Project Network]