This has multiple projects for doing Machine Learning , which includes custom code in Python, or using the SKLearn library. This also has some of the Deep Learning Implementations using Keras, TensorFlow and Theanno. Most of the files are Jupyter Notebooks but there are also some of the utility methods needed to run the Ipython notebooks. Descriptions for each of the reositories are given below.

#Deep_Learning_FASTAI_Course_1 This predominantly has all the Ipython notebooks , executed on a AWS EC2 SPU instance, for the lessons covered in the Fast AI course 1 and Fast AI course 2, the links of the lectures are given below - http://course.fast.ai/lessons/lessons.html This also has appkying Deep Learning to some of the Kaggle Data Sets for getting and competing into the Kaggle competitions, like Dog Cat redux, State Farm challenge, Fish challenge and many more.

#Deep_Learning_Using_Keras This includes a basic implementations of a Deep Neural network using Keras, and getting a 99% accuracy on the IRIS dataset, to also much more implementations using the Keras library for some other tasks.

#Python_Machine_Learning This repostories contain all the implementations in plain python from scratch , whether it is a custom Perceptron Implementation written in Python, or feature Scaling, and usage of Libraries including SkLearn, NumPy and Matplotlib.