Chapter 1: Understanding Machine Learning and Neural Networks

01 What is machine learning?
02 Map Learning Process
03 Neural Network Model
04 Learning and Assessment

Chapter 2: Machine Learning-Based Problem Solving: Using Orange and Google Colab

01 Introducing Orange
02 Installing the Orange Program
03 Orange Program Screen
04 Using Google Colab
05 Major Python Libraries for Data Analysis
06 Machine Learning-Based Problem Solving Process

Chapter 3: What services should hotel owners focus on to increase customer satisfaction?

01 Defining the Problem
02 How do we collect data?
03 How to create a hotel satisfaction classification model?
04 Implementing a Decision Tree Model Using Orange
05 Implementing a Decision Tree Model Using Python

Chapter 4 How can we classify penguin species?

01 Defining the Problem
02 How is data collected?
03 How to create a penguin species classification model?
04 Predicting Penguin Species Using Oranges
05 Predicting Penguin Species Using the Nearest Neighbor Algorithm in Python

Chapter 5: Can we predict the price of jajangmyeon in 2040?

01 Defining the Problem
02 How is data collected?
03 What data processing is required?
04 How to create a jajangmyeon price prediction model?
05 Implementing a Linear Regression Model Using Orange
06 Implementing a Linear Regression Model Using Python

Chapter 6: Can Knowing Your Health Status Predict Diabetes?

01 Defining the Problem
02 How is data collected?
03 How to create a diabetes classification model?
04 Predicting Diabetes Using Oranges
05 Predicting Diabetes Using Python

Chapter 7: Can we cluster earthquake data from around the world?

01 Defining the Problem
02 How is data collected?
03 How to create an earthquake clustering model?
04 Clustering Earthquake Data Using Orange
05 Clustering Earthquake Data Using Python

Chapter 8: How can we reduce image size using artificial intelligence?

01 Defining the Problem
02 How is data collected?
03 How to create a clustering model for photos?
04 Clustering Photo Data Using Oranges
05 Clustering Photo Data Using Python

Chapter 9: What is the connection to grocery shopping?

01 Defining the Problem
02 How is data collected?
03 How to conduct correlation analysis of grocery purchases?
04 Analyzing Grocery Purchase Relationships Using Oranges
05 Analyzing Grocery Store Relationships Using Python

Chapter 10 Can sonar detect mines and rocks?

01 Defining the Problem
02 How is data collected?
03 How to create a model to classify mines and rocks?
04 Predicting mines and rocks using oranges
05 Predicting mines and rocks using Python

Chapter 11: How Can Artificial Intelligence Help Apple Farmers?

01 Defining the Problem
02 How is data collected?
03 How to create a rotten apple classification model?
04 Sorting Rotten Apples Using Oranges
05 Predicting Rotten Apple Classification Using Python

This book describes the process of solving problems using machine learning and deep learning models, using the data mining platform Orange and Python (sklearn, keras) programming.

In this book, you'll learn how machine learning can solve real-world problems and predict future changes.
Additionally, by working on nine diverse projects, you'll experience the joy of understanding complex theories and applying them directly to real-world problems.

The process of solving problems using artificial intelligence (machine learning) to predict the future

It can be solved in the following order: [Define the problem] - [Collect data] - [Exploratory data analysis and preprocessing] - [Create a model] - [Evaluate and predict the model] - [Use the model].

That is, it is structured so that you can practice collecting and analyzing data, creating models, evaluating and predicting performance, and then utilizing the models by applying the project development stages.