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Content Introduction· · · · · ·
Machine learning is one of the hottest areas in recent years, and the Python language has evolved into one of the mainstream programming languages over time. This book combines the two hot areas of machine learning and the Python language, using two core machine learning algorithms to maximize the benefits of the Python language in data analysis.
There are 10 chapters in the book. The 1th chapter explains the Python machine learning ecosystem, the remaining 9 chapters introduce many algorithms related to machine learning, including various classification algorithms, data visualization technology, recommendation engine, etc., mainly including machine learning in apartments, air tickets, IPO market, news sources, content promotion, stock market, image, Applications such as chat bots and recommendation engines.
This book is suitable for Python programmers, data analysts, readers interested in algorithms, practitioners in the Machine learning field, and researchers reading.
Author profile ...
Alexander T. Combs is an experienced data scientist, strategist, and developer. He has a background in financial data extraction, natural language processing and generation, as well as quantitative and statistical modelling. He is currently a full-time senior lecturer in the New York immersive data science project.
Table of Contents, Chapter 1th Python machine learning ecosystem 1
1.1 Data Science/machine learning Workflow 2
1.1.1 Get 2
1.1.2 Inspection and Exploration 2
1.1.3 Cleanup and Preparation 3
1.1.4 Modeling 3
1.1.5 Evaluation 3
1.1.6 Deployment 3
1.2Python Libraries and features 3
1.2.1 Get 4
1.2.2 Check 4
1.2.3 Preparation 20
1.2.4 Modeling and Evaluation 26
1.2.5 Deployment 34
1.3 Setting up the machine learning environment 34
1.4 Summary 34
The 2nd chapter builds the app and finds low priced apartments 35
2.1 Get Apartment Listings Data 36
Using Import.io to crawl listings data 36
2.2 Checking and preparing data 38
2.2.1 Analysis Data 46
2.2.2 Visualizing Data 50
2.3 Modeling Data 51
2.3.1 Forecast 54
2.3.2 Extension Model 57
2.4 Summary 57
The 3rd chapter constructs the application, discovers the low price ticket 58
3.1 Get ticket price data 59
3.2 Using advanced web crawler technology to retrieve fare data 60
3.3 Parsing the DOM to extract pricing data 62
Identification of abnormal fares by clustering technology 66
3.4 Sending real-time reminders using IFTTT 75
3.5 Integration Together 78
3.6 Summary 82
The 4th chapter uses logistic regression to forecast IPO market 83
4.1IPO Market 84
4.1.1 What is IPO84
4.1.2 Recent IPO Market performance 84
4.1.3 Basic IPO Strategy 93
4.2 Feature Engineering 94
4.3 Two USD category 103
4.4 Importance of the characteristics 108
4.5 Summary 111
5th. Create a custom newsfeed 112
5.1 Using the Pocket application, create a collection of supervised training 112
5.1.1 Installing Pocket Chrome Extensions 113
5.1.2 using POCKETAPI to retrieve stories 114
5.2 Downloading the contents of a story using EMBED.LYAPI 119
5.3 Natural Language Processing Basics 120
5.4 Support Vector Machine 123
5.5IFTTT integration with article sources, Google forms, and e-mail 125
Set up news feeds and Google forms with IFTTT 125
5.6 Set up your daily personalised newsletter 133
5.7 Summary 137
The 6th chapter predicts whether your content will be widely circulated 138
6.1 About virology, research tells us what 139
6.2 Get the number and content of shares 140
6.3 Exploring the characteristics of the propagation 149
6.3.1 Exploring Image Data 149
6.3.2 Explore Heading 152
6.3.3 exploring the content of the story 156
6.4 Building a predictive model for content scoring 157
6.5 Summary 162
The 7th Chapter uses machine learning to predict the stock market 163
7.1 Types of market Analysis 164
7.2 About the stock market, research tells us what 165
7.3 How to develop a trading strategy 166
7.3.1 extension of our analysis cycle 172
7.3.2 using support vector regression to build our model 175
7.3.3 modeling and dynamic Time Warp 182
7.4 Summary 186
The 8th chapter establishes the image similarity engine 187
8.1 Images of machine learning 188
8.2 Working with Images 189
8.3 Looking for similar images 191
8.4 Understanding Deep Learning 195
8.5 Building an image similarity engine 198
8.6 Summary 206
9th Chapter Build Chat Robot 207
9.1 Turing Test 207
9.2 History of the chat robot 208
9.3 Design of the chat robot 212
9.4 Build a chat robot 217
9.5 Summary 227
The 10th chapter constructs the recommendation Engine 228
10.1 Collaborative Filtering 229
10.1.1 User-based filtering 230
10.1.2 Project-based filtering 233
10.2 Content-based filtering 236
10.3 Hybrid System 237
10.4 Building the recommendation engine 238
10.5 Summary 251
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Python Machine learning Practice Guide PDF
