Learn Data Science from Scratch [eBook]

Key Features:

• Complete guide to master data science basics.
• Practical and hands-on examples in ML, deep learning, and NLP.
• Drive innovation and improve decision-making through the power of data.

Description:

Learn Data Science from Scratch equips you with the essential tools and techniques, from Python libraries to machine learning algorithms, to tackle real-world problems and make informed decisions.

This book provides a thorough exploration of essential data science concepts, tools, and techniques. Starting with the fundamentals of data science, you will progress through data collection, web scraping, data exploration and visualization, and data cleaning and pre-processing. You will build the required foundation in statistics and probability before diving into machine learning algorithms, deep learning, natural language processing, recommender systems, and data storage systems. With hands-on examples and practical advice, each chapter offers valuable insights and key takeaways, empowering you to master the art of data-driven decision-making.

By the end of this book, you will be well-equipped with the essential skills and knowledge to navigate the exciting world of data science. You will be able to collect, analyze, and interpret data, build and evaluate machine learning models, and effectively communicate your findings, making you a valuable asset in any data-driven environment.

Who this ebook is for?

This book is ideal for beginners with a basic understanding of programming, particularly in Python, and a foundational knowledge of mathematics. It is well-suited for aspiring data scientists and analysts.

Table of Contents:

1. Unraveling the Data Science Universe: An Introduction

2. Essential Python Libraries and Tools for Data Science

3. Statistics and Probability Essentials for Data Science

4. Data Mining Expedition: Web Scraping and Data Collection Techniques

5. Painting with Data: Exploration and Visualization

6. Data Alchemy: Cleaning and Preprocessing Raw Data

7. Machine Learning Magic: An Introduction to Predictive Modeling

8. Exploring Regression: Linear, Logistic, and Advanced Methods

9. Unveiling Patterns with k-Nearest Neighbors and Naïve Bayes

10. Exploring Tree-Based Models: Decision Trees to Gradient Boosting

11. Support Vector Machines: Simplifying Complexity

12. Dimensionality Reduction: From PCA to Advanced Methods

13. Unlocking Unsupervised Learning

14. The Essence of Neural Networks and Deep Learning

15. Word Play: Text Analytics and Natural Language Processing

16. Crafting Recommender Systems.

17. Data Storage Mastery: Databases and Efficient Data Management.

18. Data Science in Action: A Comprehensive End-to-end Project.