Advanced Data Analytics Using Python: With Architectural Patterns, Text and Image Classification, and Optimization Techniques, 2nd Edition
- Length: 268 pages
- Edition: 2
- Language: English
- Publisher: Apress
- Publication Date: 2022-12-21
- ISBN-10: 1484280040
- ISBN-13: 9781484280041
- Sales Rank: #0 (See Top 100 Books)
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 analytics with 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.
Table of Contents About the Authors About the Technical Reviewer Acknowledgments Introduction Chapter 1: A Birds Eye View to AI System OOP in Python Calling Other Languages in Python Exposing the Python Model as a Microservice High-Performance API and Concurrent Programming Choosing the Right Database Summary Chapter 2: ETL with Python MySQL How to Install MySQLdb? Database Connection INSERT Operation READ Operation DELETE Operation UPDATE Operation COMMIT Operation ROLL-BACK Operation Normal Forms First Normal Form Second Normal Form Third Normal Form Elasticsearch Connection Layer API Neo4j Python Driver neo4j-rest-client In-Memory Database MongoDB (Python Edition) Import Data into the Collection Create a Connection Using pymongo Access Database Objects Insert Data Update Data Remove Data Cloud Databases Pandas ETL with Python (Unstructured Data) Email Parsing Topical Crawling Crawling Algorithms Summary Chapter 3: Feature Engineering and Supervised Learning Dimensionality Reduction with Python Correlation Analysis Principal Component Analysis Mutual Information Classifications with Python Semi-Supervised Learning Decision Tree Which Attribute Comes First? Random Forest Classifier Naïve Bayes Classifier Support Vector Machine Nearest Neighbor Classifier Sentiment Analysis Image Recognition Regression with Python Least Square Estimation Logistic Regression Classification and Regression Intentionally Bias the Model to Over-Fit or Under-Fit Dealing with Categorical Data Summary Chapter 4: Unsupervised Learning: Clustering K-Means Clustering Choosing K: The Elbow Method Silhouette Analysis Distance or Similarity Measure Properties General and Euclidean Distance Squared Euclidean Distance Distance Between String-Edit Distance Levenshtein Distance Needleman–Wunsch Algorithm Similarity in the Context of a Document Types of Similarity Example of K-Means in Images Preparing the Cluster Thresholding Time to Cluster Revealing the Current Cluster Hierarchical Clustering Bottom-Up Approach Distance Between Clusters Single Linkage Method Complete Linkage Method Average Linkage Method Top-Down Approach Graph Theoretical Approach How Do You Know If the Clustering Result Is Good? Summary Chapter 5: Deep Learning and Neural Networks Backpropagation Backpropagation Approach Other Algorithms TensorFlow Network Architecture and Regularization Techniques Updatable Model and Transfer Learning Recurrent Neural Network LSTM Reinforcement Learning TD0 TDλ Example of Dialectic Learning Convolution Neural Networks Summary Chapter 6: Time Series Classification of Variation Analyzing a Series Containing a Trend Curve Fitting Removing Trends from a Time Series Analyzing a Series Containing Seasonality Removing Seasonality from a Time Series By Filtering By Differencing Transformation To Stabilize the Variance To Make the Seasonal Effect Additive To Make the Data Distribution Normal Cyclic Variation Irregular Fluctuations Stationary Time Series Stationary Process Autocorrelation and the Correlogram Estimating Autocovariance and Autocorrelation Functions Time-Series Analysis with Python Useful Methods Moving Average Process Fitting Moving Average Process Autoregressive Processes Estimating Parameters of an AR Process Mixed ARMA Models Integrated ARMA Models The Fourier Transform An Exceptional Scenario Missing Data Summary Chapter 7: Analytics at Scale Hadoop MapReduce Programming Partitioning Function Combiner Function HDFS File System MapReduce Design Pattern Summarization Pattern Filtering Pattern Join Patterns A Notes on Functional Programming Spark PySpark Updatable Machine Learning and Spark Memory Model Analytics in the Cloud Internet of Things Essential Architectural Patterns for Data Scientists Scenario 1: Hot Potato Anti-Pattern Code: Data Collector Module Scenario 2: Proxy and Layering Patterns Database Core Layer Thank You Index
Donate to keep this site alive
How to download source code?
1. Go to: https://github.com/Apress
2. In the Find a repository… box, search the book title: Advanced Data Analytics Using Python: With Architectural Patterns, Text and Image Classification, and Optimization Techniques, 2nd Edition
, sometime you may not get the results, please search the main title.
3. Click the book title in the search results.
3. Click Code to download.
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.