#KeyNote #DataScience #datanalytics #modeltrain #futureprediction

Data Analytics, we often use Model Development to help us predict future observations from the data we have.

A Model will help us understand the exact relationship between different variables and how these variables are used to predict the result.

@epythonlab

Virtual Environment(virtualenv)

virtualenv, is a tool to create isolated Python environments. We need to use virtual environments to keep the dependencies used by different Python projects separate, and to keep our global site-packages directory clean. We also go one step further and install virtualenvwrapper, a set of extensions that make using virtualenv a whole lot easier by providing simpler commands.

We have to use pip python library installation package to install both dependencies on any platform.


@epythonlab #keynote #virtualenv

I can mention many reasons that FLASK is better than DJANGO but in some cases DJANGO is better than FLASK. I usually use FLASK for doing my tasks. What do you use?

#keynote #FLASK #DJANGO @epythonlab

What is WSGI?

WSGI stands for Web server Gate way interface that is a simple calling convention for web servers to forward requests to web applications or frameworks written in the Python programming language.

#keynote #python #webapp

#Flask is a customizable Python framework that gives developers complete control over how users access data. Flask is a "micro-framework" based on Werkzeug's WSGI toolkit and Jinja 2's templating engine. It is designed as a web framework for RESTful API development.

#keynote #Flask #framework @epythonlab

Why Flask is Microframework?

Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries. It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions.

Barriers to Deep Learning

Unfortunately, deep learning is not the solution to every problem.

It has three major barriers:
1. It requires enough data- Deep learning requires a lot of data
2. It requires much computing power- for example:- Google's DeepMind AlphaGo required 1,202 CPUs and 176 GPUs.
3. You probably won't understand why certain decisions were being made, given the complexity and flexibility of these algorithms.

Application of machine learning is one that requires deep learning.
@epythonlab #keynote #deeplearning #machinelearning

TensorFlow and Scikit-learn

If you're interested in solving machine learning questions, two of the most popular open-source libraries in the world for solving these problems are TensorFlow and Scikit-learn.

TensorFlow and Scikit-learn, provide any data scientist the ability to use the most advanced techniques in supervised and unsupervised machine learning easily, and for a variety of situations. You can expect that TensorFlow and Scikit-learn will continue to be used for machine learning in both industry and academia for the foreseeable future.

@epythonlab #keynote #tensorflow #scikitlearn

Check out all #keynote

Sometimes it's unable to load Jupyter Notebook on GitHub.

More resources @epythonlab #keynote

#github

What is Pandas?

Pandas is an open source library, providing high-performance, easy-to-use data structures and data analysis tools for Python.

The DataFrame is one of Pandas' most important data structures. It's basically a way to store tabular data where you can label the rows and the columns. One way to build a DataFrame is from a dictionary and also importing from CSV(comma-separated value).

Here are the most common pandas functions for data analysis https://youtu.be/8a3Y-HT09sQ
#KeyNote #Pandas #DataFrame #DataScience