Java Questions

Java Questions

1596208980

Binary Classification Model | Data Science | Machine Learning | Python

Binary Classification is a type of classification model that have two label of classes. For example an email spam detection model contains two label of classes as spam or not spam. Most of the times the tasks of binary classification includes one label in a normal state, and another label in an abnormal state. In this article I will take you through Binary Classification in Machine Learning using Python.

I will be using the MNIST dataset, which is a set of 70,000 small images of digits handwritten by high school students and employees of the US Census Bureau. Each image is labeled with the digit it represents. This set has been studied so much that it is often called the “hello world” of Machine Learning.

Whenever people come up with new classification algorithm they are curious to see how it will perform on MNIST, and anyone who learns Machine Learning tackles this dataset sooner or later. So let’s import some libraries to start with our Binary Classification model:

# Python ≥3.5 is required
import sys
assert sys.version_info >= (3, 5)
​
# Scikit-Learn ≥0.20 is required
import sklearn
assert sklearn.__version__ >= "0.20"
​
# Common imports
import numpy as np
import os
​
# to make this notebook's output stable across runs
np.random.seed(42)
​
# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)

Scikit-Learn provides many helper functions to download popular datasets. MNIST is one of them. The following code fetches the MNIST dataset:

from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784', version=1)
mnist.keys()
dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'DESCR', 

#by aman kharwal #binary classification #data science #machine learning #python

What is GEEK

Buddha Community

Binary Classification Model | Data Science | Machine Learning | Python
Ray  Patel

Ray Patel

1625843760

Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

  • revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
  • microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
  • Anaconda 4.2 – Anaconda is an opensource Python package

#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services

akshay L

akshay L

1610872689

Data Science With Python Training | Python Data Science Course | Intellipaat

In this Data Science With Python Training video, you will learn everything about data science and python from basic to advance level. This python data science course video will help you learn various python concepts, AI, and lots of projects, hands-on demo, and lastly top trending data science and python interview questions. This is a must-watch video for everyone who wishes o learn data science and python to make a career in it.

#data science with python #python data science course #python data science #data science with python

Cyrus  Kreiger

Cyrus Kreiger

1617687120

How I'd Learn Data Science If I Were To Start All Over Again

A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was starting.

I’m aware that we all learn in different ways. Some prefer videos, others are ok with just books and a lot of people need to pay for a course to feel more pressure. And that’s ok, the important thing is to learn and enjoy it.

So, talking from my own perspective and knowing how I learn better I designed this path if I had to start learning Data Science again.

As you will see, my favorite way to learn is going from simple to complex gradually. This means starting with practical examples and then move to more abstract concepts.

#data-science #machine-learning #artificial-intelligence #python-top-story #data-science-top-story #learn-python #learn-data-science

Uriah  Dietrich

Uriah Dietrich

1618449987

How To Build A Data Science Career In 2021

For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science

Java Questions

Java Questions

1596208980

Binary Classification Model | Data Science | Machine Learning | Python

Binary Classification is a type of classification model that have two label of classes. For example an email spam detection model contains two label of classes as spam or not spam. Most of the times the tasks of binary classification includes one label in a normal state, and another label in an abnormal state. In this article I will take you through Binary Classification in Machine Learning using Python.

I will be using the MNIST dataset, which is a set of 70,000 small images of digits handwritten by high school students and employees of the US Census Bureau. Each image is labeled with the digit it represents. This set has been studied so much that it is often called the “hello world” of Machine Learning.

Whenever people come up with new classification algorithm they are curious to see how it will perform on MNIST, and anyone who learns Machine Learning tackles this dataset sooner or later. So let’s import some libraries to start with our Binary Classification model:

# Python ≥3.5 is required
import sys
assert sys.version_info >= (3, 5)
​
# Scikit-Learn ≥0.20 is required
import sklearn
assert sklearn.__version__ >= "0.20"
​
# Common imports
import numpy as np
import os
​
# to make this notebook's output stable across runs
np.random.seed(42)
​
# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)

Scikit-Learn provides many helper functions to download popular datasets. MNIST is one of them. The following code fetches the MNIST dataset:

from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784', version=1)
mnist.keys()
dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'DESCR', 

#by aman kharwal #binary classification #data science #machine learning #python