Buch, Englisch, 282 Seiten, Format (B × H): 178 mm x 254 mm
The Practical Guide for Business and Science
Buch, Englisch, 282 Seiten, Format (B × H): 178 mm x 254 mm
ISBN: 978-1-4932-2892-8
Verlag: Rheinwerk Verlag GmbH
Not a programmer by trade, but still want to use Python and AI in your day-to-day work? No problem! Learn how to use Python to analyze data, automate tasks, and work with models without getting bogged down by unnecessary busy work. You’ll learn about key tools like Visual Studio Code and Jupyter Notebooks and then jump straight into data analysis and visualization with NumPy, Pandas, and Matplotlib. Hands-on examples will have you learning to clean, filter, and transform data, perform calculations, and create charts and interactive plots—right out of the gate!
Highlights include:
1)Python fundamentals
2)Data analysis
3)Data visualization
4)Machine learning
5)Natural language processing
6)Text and sentiment analysis
7)Image recognition and classification
8)APIs and web scraping
9)Database access
10)Task automation
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
Weitere Infos & Material
1. Introduction. 13
1.1. Future Skill: Programming. 13
1.2. Python. 14
1.3. Artificial Intelligence. 15
1.4. Machine Learning. 16
1.5. Programming with the Help of AI. 17
1.6. Prompt Engineering. 19
2. Getting Started with Python. 21
2.1. Installing Python on Windows. 21
2.2. Installing Python on macOS and Linux. 22
2.3. Using Python in Interactive Mode. 22
2.4. Python Scripts. 23
2.5. Visual Studio Code and Integrated Development Environments. 23
2.6. Jupyter Notebooks. 25
3. Basics of the Python Language. 29
3.1. Variables and Data Types. 30
3.2. Comments. 31
3.3. Functions. 31
3.4. Conditionals with "if," "elif," and "else". 32
3.5. Comparison Operators. 33
3.6. Numbers. 34
3.7. The "while" Loop. 35
3.8. The “for” Loop. 36
3.9. More on "print". 37
3.10. Lists, Sets, Tuples, and Dictionaries. 38
3.11. "for" Loop for Lists and More. 39
3.12. Sample Program: Counting Words. 41
3.13. Writing Your Own Functions. 44
3.14. Pythonic Code. 47
3.15. Importing Modules and Installing Packages with "pip". 52
3.16. Virtual Environments. 54
4. Working with Files. 57
4.1. Reading and Writing Text Files. 58
4.2. Comma-Separated Values Files. 60
4.3. Managing Files. 64
4.4. Example: Text Analysis. 66
4.5. Excel Files. 68
4.6. Image Files. 70
4.7. JSON Files. 73
4.8. XML Files. 77
4.9. Configuration Files. 78
5. Data Analysis. 81
5.1. NumPy. 81
5.2. Pandas. 83
5.3. Loading Data from Files into Pandas DataFrames. 87
5.4. Data Cleaning with Pandas. 90
5.5. Calculations and Analyses with Pandas. 99
5.6. Merging Data from Multiple Sources. 104
6. Visualizations with Matplotlib. 113
6.1. Creating Plots. 113
6.2. Design Options. 115
6.3. Subplots: Multiple Plots in a Single Figure. 117
6.4. Line Charts, Bar Charts, and More. 118
6.5. Creating Charts from DataFrames. 121
6.6. Interactive Charts. 124
6.7. Zooming and Scrolling. 125
7. Machine Learning and Artificial Intelligence. 131
7.1. Predicting Numbers Using Linear Regression. 133
7.2. Linear Regression with Multiple Factors. 135
7.3. Classification Using Logistic Regression. 139
7.4. Decision Trees and Random Forests. 141
7.5. K-Nearest Neighbors. 144
7.6. Support Vector Machines. 148
7.7. Training Data, Test Data, and Model Evaluation. 151
7.8. Clustering (Unsupervised Learning). 158
8. AI in Action: Text and Image Analysis. 163
8.1. AI for Text and Language. 163
8.2. Text Analysis and Word Clouds. 164
8.3. Text Preprocessing. 166
8.4. Sentiment Analysis. 170
8.5. Recognizing Entities in Text: Named Entity Recognition. 175
8.6. Transfer Learning. 178
8.7. AI for Images. 181
8.8. Image Preprocessing: Grayscale Conversion and More. 182
8.9. Detecting Edges and Contours in Images. 185
8.10. Classic Machine Learning Methods for Images. 189
8.11. Image Classification with Deep Learning. 195
9. Using APIs. 199
9.1. API Requests with "requests". 200
9.2. API Access with Special SDKs. 205
9.3. Visualizing and Analyzing API Data. 206
9.4. ChatGPT API. 212
10. Using Python on the Web. 217
10.1. HTML. 218
10.2. Flask: A Python Web Server. 220
10.3. Interactive Web Tools with Streamlit. 222
10.4. Reading Web Content with Beautiful Soup. 225
10.5. Remotely Controlling the Browser with Selenium. 228
10.6. Sending Emails and Messenger Messages. 231
11. Databases. 235
11.1. The SQL Language and the SQLite Console. 236
11.2. Creating Tables with CREATE TABLE. 238
11.3. Querying, Inserting, Updating, and Deleting with SELECT, INSERT, UPDATE, and DELETE. 240
11.4. Accessing an SQLite Database with Python. 242
11.5. Pandas DataFrame: Reading and Writing Data from Databases. 244
12. Automating Routine Tasks. 247
12.1. Retrieving Data via the API and Storing It in a Database. 247
12.2. Creating Charts from the Database and Save as Image Files. 250
12.3. Sending an Email When There’s Something New. 252
12.4. Sending Screenshots of Web Pages via Messenger. 255
12.5. Images: Reducing File Sizes, Finding Locations, and Cleaning Up. 258
12.6. PDF: Splitting, Merging, and Consolidating Files. 261
12.7. Recognizing Text on Business Card Images Using OCR and Saving to Your Phone’s Address Book. 263
12.8. Creating Invoices, Mail Merges, and Other Documents. 265
12.9. Running Python Scripts on a Schedule. 268
12.10. Logging in Automations. 271
. The Author. 275
. Index. 27
