Pratip Samanta, Sayan Mukhopadhyay

Advanced Data Analytics Using Python

With Architectural Patterns, Text and Image Classification, and Optimization Techniques

• Broschiertes Buch

Produktbeschreibung

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment. Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You'll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You'll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning. Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analyticswith reinforcement learning. Finally, the recommender system in PySpark explains how to optimize models for a specific application. What You'll Learn Build intelligent systems for enterprise Review time series analysis, classifications, regression, and clustering Explore supervised learning, unsupervised learning, reinforcement learning, and transfer learning Use cloud platforms like GCP and AWS in data analytics Understand Covers design patterns in Python Who This Book Is For Data scientists and software developers interested in the field of data analytics.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Apress / Apress L.P.
• 2nd ed
• Seitenzahl: 268
• Erscheinungstermin: 26. November 2022
• Englisch
• Abmessung: 235mm x 155mm x 15mm
• Gewicht: 411g
• ISBN-13: 9781484280041
• ISBN-10: 1484280040
• Artikelnr.: 63109838

Autorenporträt

Sayan Mukhopadhyay is a data scientist with more than 13 years of experience. He has been associated with companies such as Credit-Suisse, PayPal, CA Technology, CSC, and Mphasis. He has a deep understanding of data analysis applications in domains such as investment banking, online payments, online advertising, IT infrastructure, and retail. His area of expertise is applied high-performance computing in distributed and data-driven environments such as real-time analysis and high-frequency trading. Pratip Samanta is a Principal AI engineer/researcher having more than 11 years of experience. He worked in different software companies and research institutions. He has published conference papers and granted patents in AI and Natural Language Processing. He is also passionate about gardening and teaching.  

Inhaltsangabe

CHAPTER 1: Overview of Python Language 
1.1 Philosophy of Python programming
1.2 Comparison with other languages
1.4 Design patterns in Python
      1.4.1 Structural patterns
      1.4.2 Behavioral patterns 
      1.4.3 Creational patterns
1.5 Why Python is so popular?
1.6 Use-case where Python does not fit well
1.7 Interfacing Python with other languages
      1.7.1 Running Stanford NLP Java library in Python
      1.7.2 Running time series Holt- Winter R module in Python
      1.7.3 Expose your Python program as service in 2 minutes
1.8  Essential architectural pattern in data analytics
1. Hot Potato anti pattern
2. Data collector as a service
3. Bridge & proxy patterns.
4. Application layering
CHAPTER 2: ETL with Python 
2.1 Introduction 
2.2 Python &Mysql
2.3 Python & Neo4j
2.4 Python & Elastic Search
2.5 Crawling with Beautiful Soup
2.6 Crawling using selenium
2.7 Regular expressions
2.8 Panda framework
2.9 Cloud Storages 
2.9.1 AWS storage
2.10.1  GCP storages
2.9 Topical crawling
    2.9.1 Find potential activists for a political party from web
CHAPTER 3: Supervised Learning and Unsupervised Learning with Python 
3.1. Introduction
3.2 Correlation analysis
3.2.1 Measures of correlation
3.2.2 Threshold for correlation
3.2.3 Dealing uneven cordiality of features
3.3 Principle component analysis
          3.3.1 Singular value decomposition algorithm
         3. 3.2 Factor analysis
         3.3.3  Use case: Measuring impact of change in organization
3.4 Mutual information & dealing with categorical data
         3.4.1 Use case: Measuring most significant features in ad price
prediction
3.5 Feature engineering in texts and images 
3.5.1 Classification
3. 5.2 Decision tree & entropy gain
3. 5.3 Random forest classifier
3. 5.4 Naïve bay's classifier
3. 5.5 Support vector machine
3. 5.6 Text classification using Python
3. 5.7 Image classification using Python 
3. 5.8 Supervised & unsupervised learning
3. 5.9. Semi supervised learning
3. 6.1 Regression
3. 6.2 Least-square estimation
3. 6.3 Logistic regression
3. 6.4 Classification using regression
3.6.5 Feature scaling
3.6.6     Intentionally bias the model to over fit or under fit
CHAPTER 4: Clustering with Python 
4.1 Introduction
4.2 Distance measures
4.3 Hierarchical clustering 
      4.3.1 Top to bottom algorithm
      4.3.2 Bottom to top algorithm
4.3.3 Dendrogram to cluster
      4.3.4 Choosing the threshold
4.4 K-Mean clustering
      4.4.1 Algorithm
      4.4.2 Choosing K
4.5 Graph theoretic approach
4.6 Measure for good clustering
4.7 Find summary of a paragraph
4.8 Find faces in images
CHAPTER 5: Deep Learning & Neural Networks 
5.1 History
5.2 Architecture
5.3 Use-case where NN fit well
5.4 Back propagation algorithm 
5.5 Quick tour to other NN algorithms
5.6 Regularization techniques
5.7 Recurrent neural network
5.8 Goal oriented dialog system
5. 9.1 Convolution neural network
5. 9.2 Fake image detection
Introduction to reinforcement learning
1. Dancing Floor on GCP
2. Dialectic Learning
CHAPTER 6: Time Series Analysis 
6.1 Introduction
6.2 Smoothing techniques
6.3 Autoregressive model
6.4 Moving average model
6.5 ARMA model
6.6 ARIMA model
6.7. SARIMA model
6.8 Historical practice
6.9 Frequency domain analysis in time series 
CHAPTER 7:  Analytics in Scale 
7.1 Introduction
7.2 Hadoop architecture
7.3 Popular design pattern in MapReduce
7.4 Introduction to cloud
7.5. Analytics on cloud
7.6 Introduction to Spark
7.7. Spark architecture
- Memory optimization
- Problem with memory optimization
- Essential parameter in Spark
- Naïve Bayes classifier in Spark
7.8 A recommendation system in Spark