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#!/bin/bash

##################################################################################
#     ----------------------------------------------------------------
#     THIS SCRIPT WILL HELP YOUR AUTOMATE THE DOCKER INSTALATION STEPS
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# Test was run via aws ec2 instance. 
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# AUTHOR:
# Name: Allie Silver Ubisse 

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#!/bin/bash

##################################################################################
#     ----------------------------------------------------------------
#     THIS SCRIPT WILL HELP YOUR AUTOMATE THE DOCKER INSTALATION STEPS
#     ----------------------------------------------------------------
# Test was run via aws ec2 instance. 
#
# AUTHOR:
# Name: Allie Silver Ubisse 

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1.	Introduction

	What do we understand when we talk about the term Machine-Learning in today’s perspective of Technology? What can we achieve through means of complex algorithms? 
Simple answer to these questions comes from the need to recognize patterns, make predictions and the ability of a machine to operate over data without having to give static program instructions to it. Machine Learning is the field of computer science that gives machines/computers the ability to learn without being explicitly programmed. It is employed in a range of computing tasks where designing & programming explicit algorithms with great performance is infeasible, this includes email filtering, intruder detection in networks, computer vision, optical character recognition (OCR), etc.
Machine learning is considered to be closely related to computational statistics which as we know focuses on prediction-making through the use of computers. It is also conflated with Data mining because of the exploratory data analysis involved in b

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This document summarizes some potentially useful papers and code repositories on Sentiment analysis / document classification

• CNN:

  1. Related Paper: Convolutional Neural Networks for Sentence Classification. EMNLP 2014

     A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

     Code:

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Awesome Data Science with Python

Core - Ce qu'on va utiliser

pandas
scikit-learn

plotly-express

matplotlib
seaborn

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with mlflow.start_run(experiment_id=1, run_name="top_lever_run") as run:
    with mlflow.start_run(experiment_id=1, run_name="subrun1",nested=True) as subrun1:
        mlflow.log_param("p1","red")
        mlflow.log_metric("m1", 5.1)
        with mlflow.start_run(experiment_id=1, run_name="subsubrun1",nested=True) as subsubrun1:
            mlflow.log_param("p3","green")
            mlflow.log_metric("m3", 5.24)
        with mlflow.start_run(experiment_id=1, run_name="subsubrun2", nested=True) as subsubrun2:
            mlflow.log_param("p4","blue")
            mlflow.log_metric("m5", 3.25)