Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analytic
Table of Contents
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
