Friday, 21 July 2023

List of top 10 data science books using Python in 2023


1. "Python for Data Analysis" by Wes McKinney - This book focuses on data manipulation and analysis using Python's pandas library.

Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process. 

Download -  Python for Data Analysis

2. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron - A practical guide to machine learning using Python libraries like Scikit-Learn, Keras, and TensorFlow.

Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.

Use Scikit-learn to track an example ML project end to end

Explore several models, including support vector machines, decision trees, random forests, and ensemble methods

Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection

Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers

Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning

Download - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

3.  "Data Science from Scratch" by Joel Grus - A beginner-friendly introduction to data science concepts and tools using Python.

To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.

Get a crash course in Python

Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science

Collect, explore, clean, munge, and manipulate data

Dive into the fundamentals of machine learning

Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering

Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Download - Data Science from Scratch: First Principles with Python

4. "Python Data Science Handbook" by Jake VanderPlas - Covers essential data science libraries in Python, such as NumPy, pandas, Matplotlib, and Scikit-Learn.

Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, scikit-learn, and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you'll learn how:

IPython and Jupyter provide computational environments for scientists using Python

NumPy includes the ndarray for efficient storage and manipulation of dense data arrays

Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data

Matplotlib includes capabilities for a flexible range of data visualizations

Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms

Download  -  Python Data Science Handbook

"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - A comprehensive reference on deep learning techniques and applications.

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. 

Download        -  Deep Learning (Adaptive Computation and Machine Learning series)

"Data Science for Business" by Foster Provost and Tom Fawcett - Explores the intersection of data science and business decision-making. 

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.

Understand how data science fits in your organization—and how you can use it for competitive advantage

Treat data as a business asset that requires careful investment if you’re to gain real value

Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way

Learn general concepts for actually extracting knowledge from data

Apply data science principles when interviewing data science job candidates

Download - Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

"Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili - A hands-on guide to machine learning with Python and its libraries.

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. 

Download   -     Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

"Practical Statistics for Data Scientists" by Andrew Bruce and Peter Bruce - Provides a practical understanding of statistical concepts for data analysis.

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

With this book, you’ll learn:

Why exploratory data analysis is a key preliminary step in data science

How random sampling can reduce bias and yield a higher-quality dataset, even with big data

How the principles of experimental design yield definitive answers to questions

How to use regression to estimate outcomes and detect anomalies

Key classification techniques for predicting which categories a record belongs to

Statistical machine learning methods that "learn" from data

Unsupervised learning methods for extracting meaning from unlabeled data

Download - Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

Sunday, 9 July 2023

100 Python Interview questions

  1.  Python Program to Print Hello world!
  2. Python Program to Add Two Numbers
  3. Python Program to Find the Square Root
  4. Python Program to Calculate the Area of a Triangle
  5. Python Program to Solve Quadratic Equation
  6. Python Program to Swap Two Variables
  7. Python Program to Generate a Random Number
  8. Python Program to Convert Kilometers to Miles
  9. Python Program to Convert Celsius To Fahrenheit
  10. Python Program to Check if a Number is Positive, Negative or 0
  11. Python Program to Check if a Number is Odd or Even
  12. Python Program to Check Leap Year
  13. Python Program to Find the Largest Among Three Numbers
  14. Python Program to Check Prime Number
  15. Python Program to Print all Prime Numbers in an Interval
  16. Python Program to Find the Factorial of a Number
  17. Python Program to Display the multiplication Table
  18. Python Program to Print the Fibonacci sequence
  19. Python Program to Check Armstrong Number
  20. Python Program to Find Armstrong Number in an Interval
  21. Python Program to Find the Sum of Natural Numbers
  22. Python Program to Display Powers of 2 Using Anonymous Function
  23. Python Program to Find Numbers Divisible by Another Number
  24. Python Program to Convert Decimal to Binary, Octal and Hexadecimal
  25. Python Program to Find ASCII Value of Character
  26. Python Program to Find HCF or GCD
  27. Python Program to Find LCM
  28. Python Program to Find the Factors of a Number
  29. Python Program to Make a Simple Calculator
  30. Python Program to Shuffle Deck of Cards
  31. Python Program to Display Calendar
  32. Python Program to Display Fibonacci Sequence Using Recursion
  33. Python Program to Find Sum of Natural Numbers Using Recursion
  34. Python Program to Find Factorial of Number Using Recursion
  35. Python Program to Convert Decimal to Binary Using Recursion
  36. Python Program to Add Two Matrices
  37. Python Program to Transpose a Matrix
  38. Python Program to Multiply Two Matrices
  39. Python Program to Check Whether a String is Palindrome or Not
  40. Python Program to Remove Punctuations From a String
  41. Python Program to Sort Words in Alphabetic Order
  42. Python Program to Illustrate Different Set Operations
  43. Python Program to Count the Number of Each Vowel
  44. Python Program to Merge Mails
  45. Python Program to Find the Size (Resolution) of a Image
  46. Python Program to Find Hash of File
  47. Python Program to Create Pyramid Patterns
  48. Python Program to Merge Two Dictionaries
  49. Python Program to Safely Create a Nested Directory
  50. Python Program to Access Index of a List Using for Loop
  51. Python Program to Flatten a Nested List
  52. Python Program to Slice Lists
  53. Python Program to Iterate Over Dictionaries Using for Loop
  54. Python Program to Sort a Dictionary by Value
  55. Python Program to Check If a List is Empty
  56. Python Program to Catch Multiple Exceptions in One Line
  57. Python Program to Copy a File
  58. Python Program to Concatenate Two Lists
  59. Python Program to Check if a Key is Already Present in a Dictionary
  60. Python Program to Split a List Into Evenly Sized Chunks
  61. Python Program to Parse a String to a Float or Int
  62. Python Program to Print Colored Text to the Terminal
  63. Python Program to Convert String to Datetime
  64. Python Program to Get the Last Element of the List
  65. Python Program to Get a Substring of a String
  66. Python Program to Print Output Without a Newline
  67. Python Program Read a File Line by Line Into a List
  68. Python Program to Randomly Select an Element From the List
  69. Python Program to Check If a String Is a Number (Float)
  70. Python Program to Count the Occurrence of an Item in a List
  71. Python Program to Append to a File
  72. Python Program to Delete an Element From a Dictionary
  73. Python Program to Create a Long Multiline String
  74. Python Program to Extract Extension From the File Name
  75. Python Program to Measure the Elapsed Time in Python
  76. Python Program to Get the Class Name of an Instance
  77. Python Program to Convert Two Lists Into a Dictionary
  78. Python Program to Differentiate Between type() and isinstance()
  79. Python Program to Trim Whitespace From a String
  80. Python Program to Get the File Name From the File Path
  81. Python Program to Represent enum
  82. Python Program to Return Multiple Values From a Function
  83. Python Program to Get Line Count of a File
  84. Python Program to Find All File with .txt Extension Present Inside a Directory
  85. Python Program to Get File Creation and Modification Date
  86. Python Program to Get the Full Path of the Current Working Directory
  87. Python Program to Iterate Through Two Lists in Parallel
  88. Python Program to Check the File Size
  89. Python Program to Reverse a Number
  90. Python Program to Compute the Power of a Number
  91. Python Program to Count the Number of Digits Present In a Number
  92. Python Program to Check If Two Strings are Anagram
  93. Python Program to Capitalize the First Character of a String
  94. Python Program to Compute all the Permutation of the String
  95. Python Program to Create a Countdown Timer
  96. Python Program to Count the Number of Occurrence of a Character in String
  97. Python Program to Remove Duplicate Element From a List
  98. Python Program to Convert Bytes to a String

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