Imputation is used when handling pre-processing training data in machine learning. It is useful in handling missing data.
Saturday, May 1, 2021
Installation - Machine Learning Deep Learning Prerequisites
import numpy as np # linear algebra
import seaborn as sns # data visualization, API
from bs4 import BeautifulSoup as soup # web scraping
Install packages based on requirements.txt using command line
Install requirements
$sudo pip install -r requirements.txt
Other commonly used libraries:
numpy, scipy - for scientific computing, matplotlib,
import os
# import the os module
# "This module provides a portable way of
# using operating system dependent functionality."
Other scikit-learn import statements you might see in the wild:
from sklearn.metrics import roc_auc_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
Machine Learning in the Cloud
Workflow : How to generate or collect, preprocess and train with data.
Sample tasks :
- train machine learning models in google cloud.
- Data collection in Google Cloud or on Amazon Web Services (AWS).
- Analyze, preprocess training data.
- Clean, analyze data and present your findings
- Pre-processing data using python
- Train a basic machine learning model
- Deploy a model for prediction using a REST API
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