Web Scraping With Python Collecting More Data From The Modern Web 2nd Edition Pdf

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About Web Scraping With Python Collecting Data From The Modern Web Pdf

Ideal for programmers, security professionals, and web administrators familiar with Python, the Web Scraping With Python Collecting More Data From The Modern Web 2nd Edition Pdf not only teaches basic web scraping mechanics, but also delves into more advanced topics, such as analyzing raw data or using scrapers for frontend website testing. Code samples are available to help you understand the concepts in practice in this web scraping with python collecting data from the modern web book.

If programming is magic then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. The expanded edition of this Web Scraping With Python Collecting More Data From The Modern Web 2nd Edition Pdfnot only introduces you web scraping, but also serves as a comprehensive guide to scraping almost every type of data from the modern web.

Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server’s response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you’re likely to encounter.Parse complicated HTML pages Develop crawlers with the Scrapy framework Learn methods to store data you scrape Read and extract data from documents Clean and normalize badly formatted data Read and write natural languages Crawl through forms and logins Scrape JavaScript and crawl through APIs Use and write image-to-text software Avoid scraping traps and bot blockers Use scrapers to test your website

  • Learn how to parse complicated HTML pages
  • Traverse multiple pages and sites
  • Get a general overview of APIs and how they work
  • Learn several methods for storing the data you scrape
  • Download, read, and extract data from documents
  • Use tools and techniques to clean badly formatted data
  • Read and write natural languages
  • Crawl through forms and logins
  • Understand how to scrape JavaScript
  • Learn image processing and text recognition

table of contents

Title Page
Copyright and Credits
40 Algorithms Every Programmer Should Know
Dedication
About Packt
Why subscribe?
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Section 1: Fundamentals and Core Algorithms
Overview of Algorithms
What is an algorithm?
The phases of an algorithm
Specifying the logic of an algorithm
Understanding pseudocode
A practical example of pseudocode
Using snippets
Creating an execution plan
Introducing Python packages
Python packages
The SciPy ecosystem
Implementing Python via the Jupyter Notebook
Algorithm design techniques
The data dimension
Compute dimension
A practical example
Performance analysis
Space complexity analysis
Time complexity analysis
Estimating the performance
The best case
The worst case
The average case
Selecting an algorithm
Big O notation
Constant time (O(1)) complexity
Linear time (O(n)) complexity
Quadratic time (O(n2)) complexity
Logarithmic time (O(logn)) complexity
Validating an algorithm
Exact, approximate, and randomized algorithms
Explainability
Summary
Data Structures Used in Algorithms
Exploring data structures in Python
List
Using lists
Lambda functions
The range function
The time complexity of lists
Tuples
The time complexity of tuples
Dictionary
The time complexity of a dictionary
Sets
Time complexity analysis for sets
DataFrames
Terminologies of DataFrames
Creating a subset of a DataFrame
Column selection
Row selection
Matrix
Matrix operations
Exploring abstract data types
Vector
Stacks
The time complexity of stacks
Practical example
Queues
The basic idea behind the use of stacks and queues
Tree
Terminology
Types of trees
Practical examples
Summary
Sorting and Searching Algorithms
Introducing Sorting Algorithms
Swapping Variables in Python
Bubble Sort
Understanding the Logic Behind Bubble Sort
A Performance Analysis of Bubble Sort
Insertion Sort
Merge Sort
Shell Sort
A Performance Analysis of Shell Sort
Selection Sort
The performance of the selection sort algorithm
Choosing a sorting algorithm
Introduction to Searching Algorithms
Linear Search
The Performance of Linear Search
Binary Search
The Performance of Binary Search
Interpolation Search
The Performance of Interpolation Search
Practical Applications
Summary
Designing Algorithms
Introducing the basic concepts of designing an algorithm
Concern 1 – Will the designed algorithm produce the result we expect?
Concern 2 – Is this the optimal way to get these results?
Characterizing the complexity of the problem
Concern 3 – How is the algorithm going to perform on larger datasets?
Understanding algorithmic strategies
Understanding the divide-and-conquer strategy
Practical example – divide-and-conquer applied to Apache Spark
Understanding the dynamic programming strategy
Understanding greedy algorithms
Practical application – solving the TSP
Using a brute-force strategy
Using a greedy algorithm
Presenting the PageRank algorithm
Problem definition
Implementing the PageRank algorithm
Understanding linear programming
Formulating a linear programming problem
Defining the objective function
Specifying constraints
Practical application – capacity planning with linear programming
Summary
Graph Algorithms
Representations of graphs
Types of graphs
Undirected graphs
Directed graphs
Undirected multigraphs
Directed multigraphs
Special types of edges
Ego-centered networks
Social network analysis
Introducing network analysis theory
Understanding the shortest path
Creating a neighborhood
Triangles
Density
Understanding centrality measures
Degree
Betweenness
Fairness and closeness
Eigenvector centrality
Calculating centrality metrics using Python
Understanding graph traversals
Breadth-first search
Initialization
The main loop
Depth-first search
Case study – fraud analytics
Conducting simple fraud analytics
Presenting the watchtower fraud analytics methodology
Scoring negative outcomes
Degree of suspicion
Summary
Section 2: Machine Learning Algorithms
Unsupervised Machine Learning Algorithms
Introducing unsupervised learning
Unsupervised learning in the data-mining life cycle
Current research trends in unsupervised learning
Practical examples
Voice categorization
Document categorization
Understanding clustering algorithms
Quantifying similarities
Euclidean distance
Manhattan distance
Cosine distance
K-means clustering algorithm
The logic of k-means clustering
Initialization
The steps of the k-means algorithm
Stop condition
Coding the k-means algorithm
Limitation of k-means clustering
Hierarchical clustering
Steps of hierarchical clustering
Coding a hierarchical clustering algorithm
Evaluating the clusters
Application of clustering
Dimensionality reduction
Principal component analysis
Limitations of PCA
Association rules mining
Examples of use
Market basket analysis
Association rules
Types of rule
Trivial rules
Inexplicable rules
Actionable rules
Ranking rules
Support
Confidence
Lift
Algorithms for association analysis
Apriori Algorithm
Limitations of the apriori algorithm
FP-growth algorithm
Populating the FP-tree
Mining Frequent Patterns
Code for using FP-growth
Practical application– clustering similar tweets together
Topic modeling
Clustering
Anomaly-detection algorithms
Using clustering
Using density-based anomaly detection
Using support vector machines
Summary
Traditional Supervised Learning Algorithms
Understanding supervised machine learning
Formulating supervised machine learning
Understanding enabling conditions
Differentiating between classifiers and regressors
Understanding classification algorithms
Presenting the classifiers challenge
The problem statement
Feature engineering using a data processing pipeline
Importing data
Feature selection
One-hot encoding
Specifying the features and label
Dividing the dataset into testing and training portions
Scaling the features
Evaluating the classifiers
Confusion matrix
Performance metrics
Understanding overfitting
Bias
Variance
Bias-variance trade-off
Specifying the phases of classifiers
Decision tree classification algorithm
Understanding the decision tree classification algorithm
Using the decision tree classification algorithm for the classifiers challenge
The strengths and weaknesses of decision tree classifiers
Strengths
Weaknesses
Use cases
Classifying records
Feature selection
Understanding the ensemble methods
Implementing gradient boosting with the XGBoost algorithm
Using the random forest algorithm
Training a random forest algorithm
Using random forest for predictions
Differentiating the random forest algorithm from ensemble boosting
Using the random forest algorithm for the classifiers challenge
Logistic regression
Assumptions
Establishing the relationship
The loss and cost functions
When to use logistic regression
Using the logistic regression algorithm for the classifiers challenge
The SVM algorithm
Using the SVM algorithm for the classifiers challenge
Understanding the naive Bayes algorithm
Bayes, theorem
Calculating probabilities
Multiplication rules for AND events
The general multiplication rule
Addition rules for OR events
Using the naive Bayes algorithm for the classifiers challenge
For classification algorithms, the winner is…
Understanding regression algorithms
Presenting the regressors challenge
The problem statement of the regressors challenge
Exploring the historical dataset
Feature engineering using a data processing pipeline
Linear regression
Simple linear regression
Evaluating the regressors
Multiple regression
Using the linear regression algorithm for the regressors challenge
When is linear regression used?
The weaknesses of linear regression
The regression tree algorithm
Using the regression tree algorithm for the regressors challenge
The gradient boost regression algorithm
Using gradient boost regression algorithm for the regressors challenge
For regression algorithms, the winner is…
Practical example – how to predict the weather
Summary
Neural Network Algorithms
Understanding ANNs
The Evolution of ANNs
Training a Neural Network
Understanding the Anatomy of a Neural Network
Defining Gradient Descent
Activation Functions
Threshold Function
Sigmoid
Rectified linear unit (ReLU)
Leaky ReLU
Hyperbolic tangent (tanh)
Softmax
Tools and Frameworks
Keras
Backend Engines of Keras
Low-level layers of the deep learning stack
Defining hyperparameters
Defining a Keras model
Choosing sequential or functional model
Understanding TensorFlow
Presenting TensorFlow’s Basic Concepts
Understanding Tensor Mathematics
Understanding the Types of Neural Networks
Convolutional Neural Networks
Convolution
Pooling
Recurrent Neural Networks
Generative Adversarial Networks
Transfer Learning
Case study – using deep learning for fraud detection
Methodology
Summary
Algorithms for Natural Language Processing
Introducing NLP
Understanding NLP terminology
Normalization
Corpus
Tokenization
Named entity recognition
Stopwords
Sentiment analysis
Stemming and lemmatization
NLTK
BoW-based NLP
Introduction to word embedding
The neighborhood of a word
Properties of word embeddings
Using RNNs for NLP
Using NLP for sentiment analysis
Case study: movie review sentiment analysis
Summary
Recommendation Engines
Introducing recommendation systems
Types of recommendation engines
Content-based recommendation engines
Finding similarities between unstructured documents
Using a co-occurrence matrix
Collaborative filtering recommendation engines
Hybrid recommendation engines
Generating a similarity matrix of the items
Generating reference vectors of the users
Generating recommendations
Understanding the limitations of recommender systems
The cold start problem
Metadata requirements
The data sparsity problem
Bias due to social influence
Limited data
Areas of practical applications
Practical example – creating a recommendation engine
Summary
Section 3: Advanced Topics
Data Algorithms
Introduction to data algorithms
Data classification
Presenting data storage algorithms
Understanding data storage strategies
Presenting the CAP theorem
CA systems
AP systems
CP systems
Presenting streaming data algorithms
Applications of streaming
Presenting data compression algorithms
Lossless compression algorithms
Understanding the basic techniques of lossless compression
Huffman coding
A practical example – Twitter real-time sentiment analysis
Summary
Cryptography
Introduction to Cryptography
Understanding the Importance of the Weakest Link
The Basic Terminology
Understanding the Security Requirements
Identifying the Entities
Establishing the Security Goals
Understanding the Sensitivity of the Data
Understanding the Basic Design of Ciphers
Presenting Substitution Ciphers
Understanding Transposition Ciphers
Understanding the Types of Cryptographic Techniques
Using the Cryptographic Hash Function
Implementing cryptographic hash functions
Understanding MD5-tolerated
Understanding SHA
An Application of the Cryptographic Hash Function
Using Symmetric Encryption
Coding Symmetric Encryption
The Advantages of Symmetric Encryption
The Problems with Symmetric Encryption
Asymmetric Encryption
The SSL/TLS Handshaking Algorithm
Public Key Infrastructure
Example – Security Concerns When Deploying a Machine Learning Model
MITM attacks
How to prevent MITM attacks
Avoiding Masquerading
Data and Model Encrpytion
Summary
Large-Scale Algorithms
Introduction to large-scale algorithms
Defining a well-designed, large-scale algorithm
Terminology
Latency
Throughput
Network bisection bandwidth
Elasticity
The design of parallel algorithms
Amdahl’s law
Conducting sequential process analysis
Conducting parallel execution analysis
Understanding task granularity
Load balancing
Locality issues
Enabling concurrent processing in Python
Strategizing multi-resource processing
Introducing CUDA
Designing parallel algorithms on CUDA
Using GPUs for data processing in Python
Cluster computing
Implementing data processing in Apache Spark
The hybrid strategy
Summary
Practical Considerations
Introducing practical considerations
The sad story of an AI Twitter Bot
The explainability of an algorithm
Machine learning algorithms and explainability
Presenting strategies for explainability
Implementing explainability
Understanding ethics and algorithms
Problems with learning algorithms
Understanding ethical considerations
Inconclusive evidence
Traceability
Misguided evidence
Unfair outcomes
Reducing bias in models
Tackling NP-hard problems
Simplifying the problem
Example
Customizing a well-known solution to a similar problem
Example
Using a probabilistic method
Example
When to use algorithms
A practical example – black swan events
Four criteria to classify an event as a black swan event
Applying algorithms to black swan events
Summary
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about the author

Ryan Mitchell is a Software Engineer at LinkeDrive in Boston, where she develops their API and data analysis tools. She is a graduate of Olin College of Engineering, and is a Masters degree student at Harvard University School of Extension Studies. Prior to joining LinkeDrive, she was a Software Engineer working on web scraping and data analysis at Abine.

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