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
- Why Choose Python for Data Science & Machine Learning
Python vs R
Widespread Use of Python in Data Analysis
Clarity - Prerequisites & Reminders
Python & Programming Knowledge
Installation & Setup
Is Mathematical Expertise Necessary? - Python Quick Review
Tips for Faster Learning - Overview & Objectives
Data Analysis vs Data Science vs Machine Learning
Possibilities
Limitations of Data Analysis & Machine Learning
Accuracy & Performance - A Quick Example
Iris Dataset
Potential & Implications - Getting & Processing Data
CSV Files
Feature Selection
Online Data Sources
Internal Data Source - Data Visualization
Goal of Visualization
Importing & Using Matplotlib - Supervised & Unsupervised Learning
What is Supervised Learning?
What is Unsupervised Learning?
How to Approach a Problem - Regression
Simple Linear Regression
Multiple Linear Regression
Decision Tree
Random Forest - Classification
Logistic Regression
K-Nearest Neighbors
Decision Tree Classification
Random Forest Classification - Clustering
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 - Artificial Neural Networks
An Idea of How the Brain Works
Potential & Constraints
Here’s an Example - Natural Language Processing
Analyzing Words & Sentiments
Using NLTK