Python is an incredibly versatile programming language that can be used for data science projects, data analytics, machine learning, and data visualization.
What is Python?
Python is said to be a high-level, general-purpose programming language that is mostly used by programmers in machine learning, software development, and data science.
Python was first released in 1991 as Python 0.9.0 by Guido van Rossum. The most popular version of Python is known as Python 2 and Python 3 is available as its latest version.
Python can be used to develop a wide variety of applications - from the web, and desktop GUI-based programs or applications to scientific and mathematical programs, machine learning, and other big data computing systems.
What is Machine learning?
Machine learning is a process of transforming data into insights in order to understand patterns for taking decisions and predictions about future events efficiently and quickly. This process is called the algorithm of Machine learning.
Machine learning-based applications are developed by using Python programming language. Due to the complex, scientific computational nature of machine learning applications, Python is considered the most suitable programming language.
This is due to its large and advanced collection of mathematical and statistical libraries, extensibility, ease of use, and wide acceptance in the scientific community. As a result, Python has become the preferred programming language for developing machine learning systems.
Machine learning is a powerful technology that can be used to improve business processes and outcomes which finally helps to save time and money.
Working principle for Machine Learning
Machine learning works on the basis of models. Machine learning models can be divided into two types:
Supervised
It is designed to learn from examples and predict future events.
Unsupervised
It is designed to learn without any guidance.
Machine learning is known as the application of Artificial Intelligence, in other words, we can say that it is a process of applying artificial intelligence (AI) to data in order to make better decisions. This can be done in a number of ways, including through predictive modeling, natural language processing (NLP), and machine learning algorithms.
Predictive modeling is the most common way that machine learning is used for business purposes. It uses AI to predict future events and then uses this information to help make better business decisions.
Natural language processing (NLP) is another popular way that machine learning is used for business purposes. NLP helps you understand human communication by understanding the words that people use when they talk about their experiences or problems. This information can then be used to help improve your customer service experience or make smarter decisions about products or services.
Common uses of Machine Learning
Machine learning is commonly used in business to identify trends and correlations in data, predict outcomes based on past events, assemble facts and figures to form a narrative, or assess customer needs.
The most common applications of machine learning in business include fraud detection and prediction, supply chain management, customer service and identifying feedback, predicting customer behavior, even understanding complex financial models, and marketing research.
What is Data Science?
Data science combines cutting-edge computing and storage technologies with data representation and transformation algorithms and scientific methodology to develop solutions for a variety of complex data analysis problems involving raw and structured data in any format.
A Data Scientist has the knowledge to solve various classes of data-driven problems. And the expertise in applying the necessary algorithms, statistics, and mathematical models to create the required solutions.
Python programming language is recognized among the most powerful and popular tools for solving problems related to data science or in other words we can say Python is the most popular and powerful programming language which is used for machine learning, data science, and scientific application programming.
What is Data Engineering?
Data Engineers build the foundation for Data Science and Machine Learning systems and solutions. Data engineers are technology experts who start with requirements identified by data scientists. These requirements drive the development of data platforms that use complex data extraction, loading, and transformation to provide structured datasets that allow the Data Scientist to focus on solving a business problem./p>
Again, Python is an essential tool in the Data Engineer's toolkit - a tool used every day to architect and operate the big data infrastructure used by data scientists.
Use of Python in Machine Learning, Data Science, and Data Engineering
The world's renowned companies use data science and machine learning to create their tasks and applications. Following are the examples of practical usage of data science and machine learning applications that are done using Python -
Netflix uses data science to understand user viewing patterns and behavioral drivers. This in turn helps Netflix understand user likes/dislikes and predict and suggest relevant items to watch.
Amazon, Walmart, etc. are heavily using data science, data mining, and machine learning to understand user preferences and buying behavior. It helps in forecasting the demand for inventory management and suggesting relevant products to users online or through email marketing.
Spotify uses data science and machine learning to recommend music to its users.
Spam programs use data science and machine learning algorithms to detect and intercept spam emails.
Best books for Machine Learning and Data Science using Python
1
Machine Learning using Python
Book Description
This book has been written to provide a solid foundation for machine learning using Python libraries by providing case studies and real-life examples.
It covers topics such as machine learning basics, introduction to Python, descriptive analysis, and predictive analysis.
It includes advanced machine learning concepts such as decision tree learning, random forests, boosting, recommendation systems, and text analysis.
The book has been written taking a balanced approach between theoretical understanding and practical applications.
All topics include real-world examples and provide a comprehensive approach to research, building, evaluating, and optimizing machine learning mod
Book details
Format: Kindle Edition, PaperbackRating: 4.4 out of 5
Author: Manaranjan Pradhan, U Dinesh Kumar
Publication Date: 1 January 2019
Publisher: Wiley
Kindle Price: Rs. 283.00*
Paperback Price: Rs. 497.00 *
*Price and stock are correct and available at the time of article publication.
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2
Python - 5 Books in 1
Book Description
This book is the complete crash course for beginners for Python programming and master Data Analysis and Analytics, Data Science and Machine Learning. The book has selected topics for simplicity, best order and learning fast and selected practice exercises and examples.
This book is a complete guide to python which covers 5 manuscripts in 1 book which are -
- Python For Beginners
- Python Advanced Programming
- Python for Data Analysis & Analytics
- Python for Data Science
- Python Machine Learning
The following main topics have been included in manuscripts 1 and 2 “Python For Beginners” and “Python Advanced Programming” -
- What is Python
- How to install Python and what is the best distribution
- What are data types and variables
- How to work with numbers in Python
- What operators there are in Python and when to use them
- How to manipulate Strings
- How to implement Program Flow Controls
- How to implement loops in Python
- What are Python lists, Tuples, Sets, Dictionaries, and how to use them
- How to create modules and functions
- How to program according to the Object-Oriented paradigm
- How to create classes
- What are and how to use Inheritance, Polymorphism, Abstraction, and Encapsulation
The following main topics have been included in manuscript 3 “Python for Data Analysis & Analytics” -
- What Data Analysis is and why it is important
- What are the different types of Data Analysis
- What are the 6 key steps of the Data Analysis process that you should follow?
- What are the applications of Data Analysis and Analytics
- How to set up the Python environment for Data Analysis
- What are and how to use Python Data Structures
- How to work with IPython/Jupyter Notebook
- How to work with NumPy
- How to visualize data with Matplotlib
- What other visualization libraries are out there
- Why is Big Data important and how to get the best out of it
- How to leverage Neural Networks for Data Analysis
The following main topics have been included in manuscript 4 “Python for Data Science” -
- What is Data Science and what does it encompass
- What are the 5 key steps of the Data Science process that you should follow
- How to set up the Python environment for Data Science
- How to work with Seaborn data visualization module
- What are the most important Machine Learning Algorithms
- How to leverage the Scikit-Learn module for Machine Learning
- How to leverage Data Science in the Cloud
- What are the most important applications of Data Science
Book details
Format: Kindle Edition, PaperbackRating: 4.5 out of 5
Author: TechExp Academy
Print Length: 456 pages
Publication Date: 14 January 2021
Text-to-Speech: Enabled
Screen Reader: Supported
Enhanced typesetting: Enabled
X-Ray: Not Enabled
Word Wise: Not Enabled
Kindle Price: Rs. 449.00*
Paperback Price: Rs. 3,675.51*
*Price and stock are correct and available at the time of article publication.
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3
Data Science and Machine Learning using Python
Book Description
The goal of this book is to introduce the concepts of the Python programming language in a clear way so that the reader can easily apply the concepts to perform data science and machine learning applications to solve real-world problems.
The book has been specifically written to serve as a textbook for undergraduate and graduate students. It can also be used by professionals who either want to work in Data Science and Machine Learning using Python or are already working in it.
Each chapter in this book includes several programming examples that provide practical knowledge of the concept. To further improve your understanding of the subject, there are a number of objective, subjective type and programming exercises at the end of each chapter.
The book aims to impart a good understanding of the concepts to familiarize the reader with techniques and applications in the field.
Following are the main features of this book -
- The language is simple and easy to understand
- Glossary of important terms at the end of each chapter
- Comprehensive exercises for better clarity
- Lots of executed programs for more practice
- Appendices to give additional information
Book details
Format: PaperbackRating: 5 out of 5
Author: Dr Reema Thareja
Print Length: 640 pages
Publication Date: 10 August 2022
Publisher: McGraw Hill
Paperback Price: Rs. 668.00*
*Price and stock are correct and available at the time of article publication.
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4
Python for Data Science For Dummies
Book Description
Python is a general-purpose programming language that is popular with data scientists. It's free, as are a number of open-source libraries to help retrieve, organize and process information.
This book is intended for data analysis beginners and covers the fundamentals of data analysis and statistics programming in Python. The book covers the Python fundamentals necessary for data analysis, including objects, functions, modules, and libraries.
The book provides the statistical background needed to get started with data science programming, including probability, random distribution, hypothesis testing, confidence intervals, and building regression models for prediction.
Book details
Format: Kindle Edition, PaperbackRating: 4.3 out of 5
Author: Luca Massaron John Paul Mueller
Print Length: 504 pages
Publication Date: 1 January 2019
Publisher: Wiley
Kindle Price: Rs. 481.65*
Paperback Price: Rs. 507.00*
*Price and stock are correct and available at the time of article publication.
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5
Introduction to Machine Learning with Python: A Guide for Data Scientists
Book Description
Machine learning has become an integral part of many business applications and research projects, but the field is not exclusive to large companies with large research teams.
If you are using Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, the applications of machine learning are limited only by your imagination.
You will learn the steps required to build a successful machine learning application with Python and the scikit-learn library through this book. This book focuses on the practical aspects of using machine learning algorithms.
Below are the highlights of this book which has been included -
- Fundamental concepts and applications of machine learning
- Advantages and shortcomings of widely used machine learning algorithms
- How to represent data processed by machine learning, including which data aspects to focus on
- Advanced methods for model evaluation and parameter tuning
- The concept of pipelines for chaining models and encapsulating your workflow
- Methods for working with text data, including text-specific processing techniques
- Suggestions for improving your machine learning and data science skills
Book details
Format: Kindle Edition, PaperbackRating: 4.5 out of 5
Author: Andreas C. Müller, Sarah Guido
Print Length: 402 pages
Publication Date: 26 September 2016
Publisher: O'Reilly Media
Simultaneous device usage: Unlimited
Text-to-Speech: Enabled
Enhanced typesetting: Enabled
X-Ray: Not Enabled
Word Wise: Not Enabled
Kindle Price: Rs. 1,140.00*
Paperback Price: Rs. 1,200.00*
*Price and stock are correct and available at the time of article publication.
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FAQ - Frequently Asked Question
What is Python for data science and machine learning?
Python is a general-purpose programming language created by Guido van Rossum. It is a high-level, object-oriented and easy-to-learn language for building high-performance applications related to machine learning and data science.
Can Python be used in machine learning?
Yes, Python language can be used for machine learning or say that Python is the language that is developed for the most applications related to machine learning tasks. Python can perform a set of complex machine learning tasks.
Why is Python better than R?
Python is a general purpose programming language that means it can be used for many purposes. Python is known for its high performance and better scalability. But there is one thing that sets it apart from R, which is data analysis and data visualization performance. R is a better option than Python for data science applications development.
Is Python sufficient for data science?
Yes, Python is good enough for data science. Python is the programming language accepted by data scientists and analysts as one of the best languages in the world for various data science applications.
Is Python good for AI?
Yes, Python is good for AI(Artificial Intelligence). Python is considered to be the most efficient programming language for creating AI based applications.
Should I learn R or Python first?
You should learn Python first because Python is becoming a progressive and dominant programming language for data science and anlysis which can compete with R in future.
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