machine-Learning_Tutorials

Basic concept of ML

Machine Learning experience

for machine Learning

#Introduction

#Required libraries

If you don't have Python on your computer, you can use the Anaconda Python distribution to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.

This notebook uses several Python packages that come standard with the Anaconda Python distribution. The primary libraries that we'll be using are:

NumPy: Provides a fast numerical array structure and helper functions.
pandas: Provides a DataFrame structure to store data in memory and work with it easily and efficiently.
scikit-learn: The essential Machine Learning package in Python.
matplotlib: Basic plotting library in Python; most other Python plotting libraries are built on top of it.
Seaborn: Advanced statistical plotting library.
tensorflow: TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them.

To make sure you have all of the packages you need, install them with conda:

conda install numpy pandas scikit-learn matplotlib seaborn

conda may ask you to update some of them if you don't have the most recent version. Allow it to do so.

Note: I will not be providing support for people trying to run this notebook outside of the Anaconda Python distribution.

Licenses

To the extent possible under law, Amol Jagadambe has waived all copyright and related or neighboring rights to this work.