Top 10 Machine Learning Tools Used By Data Scientists

Top 10 Machine Learning Tools Used By Data Scientists

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Vikram
Vikram Singh
Assistant Manager - Content
Updated on Jun 8, 2022 08:11 IST

Table of Content

Introduction

In this article we will discuss the Top 10 Machine Learning Tools used by Data Scientists.

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Machine Learning is the study of computer algorithms that can automatically learn and improve from the experience without being explicitly programmed

Machine learning algorithms are mainly classified into two categories:

  • Supervised Learning
  • Unsupervised Learning. 

Supervised Learning: 

  • It uses the labeled data to train the model to classify the data or predict the outcomes accurately. 
  • Algorithms: Linear Regression, Logistic Regression, Decision Tree, Random Forest, AdaBoost, XgBoost. 
  • Example: Classify the spam in your inbox folder, and predict the house prices

Unsupervised Learning: 

  • It uses unsupervised algorithms to analyze and cluster the unlabeled dataset.
  • These algorithms identify the hidden patterns and make the cluster to make the required conclusion. 
  • Algorithms: Principal Component Analysis, Singular Value Decomposition approaches.
  • Example: Product and customer segmentation, Similarity Detection, and Recommendation System. 

Must Check: Supervised vs Unsupervised

Must Check: What is Machine Learning?

Must Check: Machine Learning Online Courses & Certifications

Here, is the list of  Top 10 Machine Learning tools used by Data Scientists:

Numpy:

NumPy

About:

  • Stands for Numerical Python
  • Support for large and multi-dimensional array and matrices
  • Python library but uses C/C++

Advantage:

  • Much less memory is needed to store data
  • Fast Performance
  • Mathematical operations are easy to perform over

Real-Life Application of Numpy:

  • Calculator
  • Video Game
  • Random Password Generator
  • Statistical Analysis

Must Check: NumPy Interview Question

Pandas:

2022_01_pandas-git.jpg

About:

  • Data Analysis and Manipulation Tool
  • Built on top of NumPy package
  • Mainly works with the tabular data

Advantage:

  • Data Representation
  • Easy collaboration with other tools
  • Efficiently handling Large Data

Real-Life Application of Pandas:

  • Recommendation System (Netflix and Amazon)
  • Neuroscience
  • Predicting Stocks
  • Natural Language Processing

Difference Between Pandas and NumPy

2022_01_numpy-vs-Pandas.jpg

Must Check: Pandas Interview Question

Matplotlib

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About:

  • Data Visualization and Graphical Plotting Library
  • Provides object-oriented API for embedding plots
  • Open source and mostly written in Python

Advantage:

  • Cross-Platform and Portable
  • Integrated with LaTeX markup
  • Customizable and Extensible

Real-life Application of Matplotlib:

  • Neuroscience
  • Stock Price Visualization
  • Game development 

Scikit Learn

2022_01_scikitlearn-git.jpg

About:

  • Open Source Machine Learning library for Python
  • Built on NumPy, SciPy, and Matplotlib
  • Accessible and reusable

Advantage:

  • Features various classification, regression, and clustering algorithms
  • Models are trained and tested on the different datasets than one used for training data using train-test split
  • Implements the non-neural net-based algorithm

Real-Life Application of Scikit learn:

  • Predictive Analysis(JP Morgan, Booking.com)
  • Spotify (recommendation)
  • Automation(change.org)
  • Evaluate and Improve Matchmaking System (Tinder, OkCupid)

Must Check: Scikit Learn Tutorial

Tensor Flow

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About:

  • End to end open-source machine learning library
  • Developed by Google for internal research and production
  • It has a collection of workflows with intuitive high-level API’s

Advantage:

  • Easy model building
  • Robust ML production anywhere
  • Powerful experimentation for research

Real-Life Application of TensorFlow:

  • Image Classification (VSCO)
  • Face Detection Model (Modiface)
  • Object Detection (Adidas)

Difference between Scikit Learn and TensorFlow

2022_01_scikit-learn-vs-Tensorflow.jpg

PyTorch

2022_01_pytorch-git.jpg

About:

  • Open-source machine learning framework
  • Based on Torch library
  • Used in Computer Vision and Natural Language Processing

Advantage:

  • Cloud Support
  • Considered as NumPy extension of GPUs
  • Easy to Debug and Understand

Real-Life Application of PyTorch:

  • Image Recognition: Object Detection using YOLO V3
  • Salesforce: Pushing the state of art in NLP and Multi-Tasking Learning
  • Marketing(Airbnb uses Generative Adversal Network)

Difference between PyTorch and TensorFlow

2022_01_pytorch-vs-tensorflow.jpg

NLTK

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About:

  • Stands for Natural Language Toolkit
  • Used to work with human language data
  • It contains libraries and programs for statistical language processing

Advantage:

  • It fully supports the English language
  • It consists of algorithms such as tokenizing, parts of speech, stemming, topic segmentation
  • Efficient at analyzing large datasets

Real-Life Application of NLTK:

  • Sentiment Analysis (Twitter)
  • Question Answering (SQuAD, CoQA)
  •  Text Classification (Amazon, IMDB)
  • Speech Recognition (Siri, Alexa)

Jupyter Notebook

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About:

  • Web-based interactive computing platform
  • Allows to creation, share documents with interactive live codes
  • Julia, Python, and R are supported by Jupyter

Advantage:

  • Language Independent
  • Training ML models
  • Data Visualization

Real-Life Application of Jupyter:

  • Google (Search Engine)
  • O’Reilly (Recommendation System)
  • NASA (Automating Image Analysis)

Tableau

2022_01_tableau-git.jpg

About:

  • Data visualization software focused on business intelligence
  • Connects and extracts the data from an external source
  • Tools can be used without any coding knowledge

Advantage:

  • Provides beautiful dashboards and reports
  • Automate Reporting
  • Perform ETL(Explore, Transform and Load) operations quickly

Real-Life Application of Tableau:

  • Customer Behavior Insight(Sysco Labs)
  • Sales Prediction (Specialized)
  • Deployment Strategy(Red Hat)

Must Check: What is Tableau?

Must Check: Tableau Online Courses & Certifications

MATLAB

2022_01_matlab-git.jpg

About

  • Stands for Matrix Laboratory
  • Programming and Numeric Computing Platform
  • The basic data element is Matrix

Advantage:

  • Debug easily
  • Keep track of files and variables
  • Provides tools to develop GUI based applications

Real-Life Application of MATLAB:

  • Analyze and Design Antenna
  • Face Detection
  • Simulate an Artificial Neural Network

Conclusion:

These are the top 10 Machine Learning tools used by Data scientists to check out in 2022 before starting your machine learning journey. These tools can make your learning and transition into data science smooth.

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Frequently Ask Question

Q1. What are the different machine learning tools that are used by Data Scientist?

A1. Data Scientist use tools like NumPy, Pandas, MATLAB, Matplotlib, NLTK, PyTorch, Scikit Learn, Tableau and Tensor Flow and Jupyter Notebook.

Q2. What are the four different types of data that can be used in machine learning?

A2. Numerical, Categorical, time-series and text data are mostly used in the machine learning.

Q3. Do Data Scientist use Tableau?

A3. Tableau is a visual analytics platform transforming the way we use the data to solve problems empowering people and organizations to make most of their data. It is the fastest growing, powerful and most popular data visualization and business intelligence tool that allow us to analyze trends visually and take quick decision

FAQs

What are the different machine learning tools that are used by Data Scientist?

Data Scientist use tools like NumPy, Pandas, MATLAB, Matplotlib, NLTK, PyTorch, Scikit Learn, Tableau and Tensor Flow and Jupyter Notebook.

What are the four different types of data that can be used in machine learning?

Numerical, Categorical, time-series and text data are mostly used in the machine learning.

Do Data Scientist use Tableau.

Tableau is a visual analytics platform transforming the way we use the data to solve problems empowering people and organizations to make most of their data. It is the fastest growing, powerful and most popular data visualization and business intelligence tool that allow us to analyze trends visually and take quick decision

About the Author
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Vikram Singh
Assistant Manager - Content

Vikram has a Postgraduate degree in Applied Mathematics, with a keen interest in Data Science and Machine Learning. He has experience of 2+ years in content creation in Mathematics, Statistics, Data Science, and Mac... Read Full Bio

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