Data Analysis From Scratch With Python Beginner Guide using Python

Data Analysis From Scratch With Python Beginner Guide using Python

Data Analysis From Scratch With Python Beginner Guide using Python

“Mankind is very nearly computerized servitude on account of AI and biometric advancements. One approach to forestall that is to foster inbuilt modules of profound sensations of adoration and sympathy in the learning calculations.”

― Amit Ray, Compassionate Artificial Superintelligence AI 5.0 – AI with Blockchain, BMI, Drone, IoT,
also Biometric Technologies

Assuming you are searching for a total manual for the Python language and its library that will assist you with turning into a compelling information expert, this book is for you. This book contains the Python programming you really want for Data Analysis.

Also Read:- Step-by-Step Guide To Implement Machine Learning Algorithms with Python

Why the AI Sciences Books are unique?

The AI Sciences Books investigate each part of Artificial Intelligence and Data Science utilizing software engineering programming languages like Python and R. Our books might be the best one for amateurs; it’s a bit-by-bit guide for any individual who needs to begin gaining Artificial Intelligence and Data Science from scratch. It will help you in setting up a strong establishment and getting familiar with some other high-level courses will be not difficult for you.

Step by Step Guide and Visual Illustrations and Examples

The Book gives total guidelines for controlling, handling, cleaning, displaying, and crunching datasets in Python. This is an involved aide with pragmatic contextual investigations of information examination issues successfully. You will learn pandas, NumPy, IPython, and Jupiter in the Process.

Also read:-Beginning Django: Web Application Development and Deployment with Python

Who Should Read This?

This book is a useful prologue to information science devices in Python. It is great for expert novices to Python and for Python developers new to information
science and software engineering. Rather than extreme mathematical equations, this book contains a few diagrams and pictures.

Author Biography

Peters Morgan is a long-time user and developer of Python. He is one of the core developers of some data science libraries in Python. Currently, Peter works as Machine Learning Scientist at Google.

Table of Contents

  1. Why Choose Python for Data Science & Machine Learning
    Python vs R
    Widespread Use of Python in Data Analysis
  2. Prerequisites & Reminders
    Python & Programming Knowledge
    Installation & Setup
    Is Mathematical Expertise Necessary?
  3. Python Quick Review
    Tips for Faster Learning
  4. Overview & Objectives
    Data Analysis vs Data Science vs Machine Learning
    Limitations of Data Analysis & Machine Learning
    Accuracy & Performance
  5. A Quick Example
    Iris Dataset
    Potential & Implications
  6. Getting & Processing Data
    CSV Files
    Feature Selection
    Online Data Sources
    Internal Data Source
  7. Data Visualization
    Goal of Visualization
    Importing & Using Matplotlib
  8. Supervised & Unsupervised Learning
    What is Supervised Learning?
    What is Unsupervised Learning?
    How to Approach a Problem
  9. Regression
    Simple Linear Regression
    Multiple Linear Regression
    Decision Tree
    Random Forest
  10. Classification
    Logistic Regression
    K-Nearest Neighbors
    Decision Tree Classification
    Random Forest Classification
  11. Clustering
    Goals & Uses of Clustering
    K-Means Clustering
    Anomaly Detection
  12. Association Rule Learning
  13. Reinforcement Learning
    What is Reinforcement Learning?
    Comparison with Supervised & Unsupervised Learning
    Applying Reinforcement Learning
  14. Artificial Neural Networks
    An Idea of How the Brain Works
    Potential & Constraints
    Here’s an Example
  15. Natural Language Processing
    Analyzing Words & Sentiments
    Using NLTK

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