Machine Learning: The complete Math Guide to Master Data Science with Python and Developing Artificial Intelligence by Algore, Matt, pdf book , free download

## Contents:

**PART I**

Introduction

What Happens When you Try to Teach a Machine to Do Mathematics?

Logistic Regression

Decision Trees

Naive Bayes

Artificial Neural Networks

Co-Regression Models

Clustering Algorithms

Why Python and Data Science?

Is it Possible to Apply Machine Learning to Aspects of Mathematics?

Can MATLAB in a Computer Do Machine Learning?

Is it Possible to Use MATLAB in a Computer to Do Machine Learning?

How Do You Teach a Computer to Do Machine Learning?

Can You Automate Machine Learning?

Do Computers Ever Make Mistakes?

Who Is to Blame When a Machine Makes a Mistake?

Relation Between Big Data and Machine Learning (ML)

Uses of Machine Learning

Actual Machine Learning Algorithms

**Chapter 1. What Is Machine Learning?**

1. Research on Statistics

2. An Analysis of Big Data

3. The Financial World

The Benefits of Machine Learning

1. Marketing Products Are More Comfortable

2. Machine Learning Can Help with Accurate Medical Predictions

3. Can Make Data Entry Easier

4. Helps with Spam Detection

5. Can Improve the Financial World

6. Can Make Manufacturing More Efficient

7. It Requires us with a Better Understanding of the Customer

Supervised Machine Learning

Unsupervised Machine Learning

Reinforcement Machine Learning

**Chapter 2. Giving the Computers the Ability to Learn From Data**

Why Use Python for Machine Learning?

How to Get Started with Python?

Python Syntax

Python Variables

**Chapter 3. Basic Terminology and Notations**

Mathematical Notation for Machine Learning

Algebra

Calculus

Linear Algebra

Probability

Set Theory

Statistics

Terminologies Used for Machine Learning

1. Natural Language Processing (NLP)

2. Dataset

3. Computer Vision

4. Supervised Learning

5. Unsupervised Learning

6. Reinforcement Learning

7. Neural Networks

**Chapter 4. Evaluating Models and Predicting Unseen Data Instances**

How Is Python Chosen Over other Tools for Data Science?

Direct Learning

Data Science Vast Libraries

Expandable

Colossal Community for Python

Why Python and Data Science Mix Well?

Data Science Statistical Learning

Inference and Prediction

Parametric and Non-Parametric Functions

Model Interpretability and Prediction Accuracy

Model Accuracy Assessing

Variance and Bias

Variance and Bias Relationship

Relation Between Big Data and Machine Learning (ML)

**Chapter 5. Building Good Training Datasets**

Import Dataset

Preview the Dataframe

Find Row Item

Shape

Columns

Describe

Pairplots

Heatmaps

**Chapter 6. Combining Different Models for Ensemble Learning**

**Chapter 7. Applying Machine Learning to Sentiments Analysis**

1. How Would you Explain NLP to a Layman? Why Is it Difficult to Implement?

2. What Is the Use of NLP in Machine Learning?

3. What Are the Different Steps in Performing Text Classification?

4. What Do you Understand by Keyword Normalization? Why Is it Needed?

5. Tell me about Part-Of-Speech (POS) Tagging.

6. Have you Heard of the Dependency Parsing Algorithm?

7. Explain the Vector Space Model and its Use.

8. What Do you Mean by Term Frequency and Inverse Document Frequency?

9. Explain Cosine Similarity in a Simple Way.

10. Explain the N-Gram Method.

11. How Many 3-Grams Can Be Generated from this Sentence "I Love New York Style Pizza"?

12. Have you Heard of the Bag-Of-Words Model?

**Chapter 8. Conditional or Decisional Statements**

The If Statement

The If-Else Statement

The Elif Statements

Control Flow

**Chapter 9. Functions**

Why Are User-Defined Functions so Important?

Options for Function Arguments

Writing a Function

Python Modules

Python Package

**Chapter 10. Actual Machine Learning Algorithms**

An Overview on Decision Trees

Classification and Regression Trees

The Overfitting Problem

**Chapter 11. Applications of the Machine Learning Technology**

Virtual Personal Assistants

Predictions While Driving

Video Surveillance

Social Media

Email Spam and Malware Filtering

Online Customer Service

Refinement of Search Engine Results

Product Recommendations

Online Fraud Detection

Predictive Analytics

Prescient Analysis for Customer Behavior

Capability and Prioritization of Leads

Distinguishing Proof of Current Market Trends

Client Segmentation and Targeting

Advancement of Marketing Strategies

**Chapter 12. Data Mining and Applications**

How Does Data Mining Work?

Unbalanced Data Set

Conclusion

**PART II**

Introduction

Features of Python Programming

Simple Language

Portability

Standard Libraries

Free Open-Sources

Downloading and Installing Python

Python Development and Application

Python Variables

Naming Variables in Python

Types of Data Variables

Int

Char

Bytes

Strings

Python Debugging

**Chapter 1. About Data Analysis**

**Chapter 2. Why Python for Data Analysis**

How Python Can Help With Data Analysis

How Python Fits Into Data Analysis

**Chapter 3. The Steps of Data Analysis**

Defining Your Question

Setting up Clear Measurements

Collecting the Data

**Chapter 4. Libraries**

Scikit – Learn

TensorFlow

Theano

Pandas

Diagrammatic Explanations

Series Dimensional

Data Frames Dimensional

Seaborn

Diagrammatic Illustrations

NumPy

SciPy

Koras

PyTorch

Scrapy

Statsmodels

**Chapter 5. Predictive Analysis**

What a Predictive Analysis Is

**Chapter 6. Combining Libraries**

The PyTorch Library

The Beginnings of PyTorch

Reasons to Use PyTorch With the Data Analysis

Pandas

Matrix Operations

Slicing and Indexing

**Chapter 7. Machine Learning and Data Analysis**

What Machine Learning Is

Decision Trees and Random Forests

SciKit-Learn

Linear Regression

Support Vector Machines (SVM)

K-means Clustering

**Chapter 8. Applications**

Security

Transportation

Danger and Fraud Detection

Coordination of Deliveries

Client Interactions

City Planning

Medical Care

Travel

Computerized Advertising

**Chapter 9. Data Visualization and Analysis With Python**

Enormous Data

The Versus of Big information

SAS

Enormous Data Analytics

**Chapter 10. Data Science**

Data Science and Its Significance

Future of Information Technology

Information Structures

Highlights of Information Structures

Information Structure Types

Usage of Information Structures

How Critical Is the Use of Python for Data Science?

Python Data Science Uses

**Chapter 11. Data Science and the Cloud**

The Cloud

Network

Data Science in the Cloud

Software Architecture and Quality Attributes

Sharing Big Data In The Cloud

Cloud And Big Data Governance

Need For Data Cloud Tools To Deliver High Value Of Data

Conclusion

## About The Book:

Year: 2021

Language: English

Dimensions : 8 x 0.69 x 10 inches

File: **PDF**, 6.11 MB

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