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Here in this article, I will take you through how to implement a binary search algorithm with python. Binary search also called half-interval search, which is an algorithm used in computers systems to find the position of a value in a sorted array.

In a binary search algorithm, the list is split in half and then searched in each half. One thing to notice while implementing the binary search algorithm is that the list must be sorted before running the algorithm.

**Also, Read – One Hot Encoding in Machine Learning**.

The list is then split into two halves by the index, find the element in the middle(m) of the list, then start at m-1 is a list and m+1 at the end is another list, check if the element is at the middle, higher or lower than this and return the appropriate position of the key element to find.

So let’s say you have a list of 10,000 items. The element you are looking for is in the 9000th place. If you implement any other search algorithm in this scenario, it will take a long time to give you the result. Because the algorithm has to check every item in the list.

So we first need to specify the lower limit and the upper limit. The lower limit is the first index in the list and the upper limit is the last index in the list. Once you finish assigning the lower and upper limits, the next thing you have to find a mid-index.

Mid index = (Lower limit + Upper Limit)/2

Now let’s specify the item we need to find in our list. Let’s say we need to find number 90 in our list. Now let’s create a binary search algorithm with Python to find the number 90

#machine learning #python #data science

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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.

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

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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

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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

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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

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Searching for an element’s presence in a list is usually done using linear search and binary search. Linear search is time-consuming and memory expensive but is the simplest way to search for an element. On the other hand, Binary search is effective mainly due to the reduction of list dimension with each recursive function call or iteration. A practical implementation of binary search is autocompletion.

The objective of this project is to create a simple python program to implement binary search. It can be implemented in two ways: recursive (function calls) and iterative.

The project uses loops and functions to implement the search function. Hence good knowledge of python loops and function calls is sufficient to understand the code flow.

#python tutorials #binary search python #binary search python program #iterative binary search python #recursive binary search python