Book Details

  • Format: pdf
  • Size: 2.80 MB
  • Author: Peters Morgan

This book is for: This book is a practical introduction to data science tools in Python. It is ideal for analyst’s beginners to Python and for Python programmers new to data science and computer science. Instead of tough math formulas, this book contains several graphs and images.

Are you thinking of becoming a data analyst using Python? If you are looking for a complete guide to data analysis using Python language and its library that will help you to become an effective data scientist, this book is for you.

From AI Sciences Publisher Our books may be the best one for beginners; it’s a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples The Book give complete instructions for manipulating, processing, cleaning, modeling and crunching datasets in Python.

This is a hands-on guide with practical case studies of data analysis problems effectively. You will learn pandas, NumPy, IPython, and Jupiter in the Process. T
What’s Inside This Book?

  • Introduction
  • Why Choose Python for Data Science & Machine Learning
  • Prerequisites & Reminders
  • Python Quick Review
  • Overview & Objectives
  • A Quick Example
  • Getting & Processing Data
  • Data Visualization
  • Supervised & Unsupervised Learning
  • Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Decision Tree
  • Random Forest


  • Logistic Regression
  • K-Nearest Neighbors
  • Decision Tree Classification
  • Random Forest Classification


  • Goals & Uses of Clustering
  • K-Means Clustering
  • Anomaly Detection

Association Rule Learning

  • Explanation
  • Apriori

Reinforcement Learning

  • What is Reinforcement Learning
  • Comparison with Supervised & Unsupervised Learning
  • Applying Reinforcement Learning

Neural Networks

  • An Idea of How the Brain Works
  • Potential & Constraints
  • Here’s an Example

Natural Language Processing

  • Analyzing Words & Sentiments
  • Using NLTK

Model Selection & Improving Performance
Sources & References





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