Explore Data Cleaning, Pre-processing, Data Wrangling, Feature Engineering and Machine Learning using Python and Jupyter
Updated on Mar, 2021
Programming, Data Science,
Formats : PDF, EPUB (Downlodable)
ISBN : 9789389898064
Solve business problems with data-driven techniques and easy-to-follow Python examples
This book begins with an introduction to Data Science followed by the Python concepts. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you will clean the dataset and after that will gain an understanding about various visualization charts. This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready.
This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms.
This book is for a beginner or an experienced professional who is thinking about a career or a career switch to Data Science. Each chapter contains easy-to-follow Python examples.
1. Data Science Fundamentals
2. Installing Software and System Setup
3. Lists and Dictionaries
4. Package, Function, and Loop
6. Pandas and DataFrame
7. Interacting with Databases
8. Thinking Statistically in Data Science
9. How to Import Data in Python?
10. Cleaning of Imported Data
11. Data Visualization
12. Data Pre-processing
13. Supervised Machine Learning
14. Unsupervised Machine Learning
15. Handling Time-Series Data
16. Time-Series Methods
21. Python Virtual Environment
22. Introduction to An Advanced Algorithm - CatBoost
23. Revision of All Chapters’ Learning
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