Python Machine Learning Blueprints 2nd - 2019

【实例简介】Python Machine Learning Blueprints 2nd  - 2019

【实例截图】

【核心代码】

Table	of	Contents
Title	Page
Copyright	and	Credits
Python	Machine	Learning	Blueprints
Second	Edition
About	Packt
Why	subscribe?
Packt.com
Contributors
About	the	authors
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
1.	 The	Python	Machine	Learning	Ecosystem
Data	science/machine	learning	workflow
Acquisition
 
Inspection
Preparation
Modeling
Evaluation
Deployment
Python libraries and functions for each stage of the data science workflow
Acquisition
Inspection
The Jupyter Notebook
Pandas
Visualization
The matplotlib library
The seaborn library
Preparation
map
apply
applymap
groupby
Modeling and evaluation
Statsmodels
Scikit-learn
Deployment
Setting up your machine learning environment
Summary
2. Build an App to Find Underpriced Apartments
Sourcing apartment listing data
Pulling	down	listing	data
Pulling	out	the	individual	data	points
Parsing	data
Inspecting	and	preparing	the	data
Sneak-peek	at	the	data	types
Visualizing	our	data
Visualizing	the	data
Modeling	the	data
Forecasting
Extending	the	model
Summary
3.	 Build	an	App	to	Find	Cheap	Airfares
Sourcing	airfare	pricing	data
Retrieving	fare	data	with	advanced	web	scraping
Creating	a	link
Parsing	the	DOM	to	extract	pricing	data
Parsing
Identifying	outlier	fares	with	anomaly	detection	techniques
Sending	real-time	alerts	using	IFTTT
Putting	it	all	together
Summary
4.	 Forecast	the	IPO	Market	Using	Logistic	Regression
The	IPO	market
What	is	an	IPO?
Recent	IPO	market	performance
Working	on	the	DataFrame

Analyzing the data
Summarizing the performance of the stocks
Baseline IPO strategy
Data cleansing and feature engineering
Adding features to influence the performance of an IPO
Binary classification with logistic regression
Creating the target for our model
Dummy coding
Examining the model performance
Generating the importance of a feature from our model 
Random forest classifier method
Summary
5. Create a Custom Newsfeed
Creating a supervised training set with Pocket
Installing the Pocket Chrome Extension
Using the Pocket API to retrieve stories
Using the Embedly API to download story bodies
Basics of Natural Language Processing
Support Vector Machines
IFTTT integration with feeds, Google Sheets, and email
Setting up news feeds and Google Sheets through IFTTT
Setting up your daily personal newsletter
Summary
6. Predict whether Your Content Will Go Viral
What does research tell us about virality?
Sourcing shared counts and content
Exploring	the	features	of	shareability
Exploring	image	data
Clustering
Exploring	the	headlines
Exploring	the	story	content
Building	a	predictive	content	scoring	model
Evaluating	the	model
Adding	new	features	to	our	model
Summary
7.	 Use	Machine	Learning	to	Forecast	the	Stock	Market
Types	of	market	analysis
What	does	research	tell	us	about	the	stock	market?
So,	what	exactly	is	a	momentum	strategy?
How	to	develop	a	trading	strategy
Analysis	of	the	data
Volatility	of	the	returns
Daily	returns
Statistics	for	the	strategies
The	mystery	strategy
Building	the	regression	model
Performance	of	the	model
Dynamic	time	warping
Evaluating	our	trades
Summary
8.	 Classifying	Images	with	Convolutional	Neural	Networks
Image-feature extraction
 
Convolutional neural networks
Network topology
Convolutional layers and filters
Max pooling layers
Flattening
Fully-connected layers and output
Building a convolutional neural network to classify images in the Zalando Resea
rch dataset, using Keras
Summary
9. Building a Chatbot
The Turing Test
The history of chatbots
The design of chatbots
Building a chatbot
Sequence-to-sequence modeling for chatbots
Summary
10. Build a Recommendation Engine
Collaborative filtering
So, what's collaborative filtering?
Predicting the rating for the product
Content-based filtering
Hybrid systems
Collaborative filtering
Content-based filtering
Building a recommendation engine
Summary
11.	 What's	Next?
Summary	of	the	projects
Summary
Other	Books	You	May	Enjoy
Leave	a	review	-	let	other	readers	know	what	you	think