Machine Learning: Must Do Projects for Beginners
Popular Datasets in Machine Learning, Popular Models in Machine Learning
Machine learning is a branch of artificial intelligence that makes predictions or decisions by using algorithms to learn from data. It’s crucial to have a solid understanding of both the datasets you will be dealing with and the models you can use to construct your models before you begin using machine learning. We will examine some of the most well-liked machine learning datasets and models in this article. In the next sections, we will discuss several datasets and the models that may be used in that exercises.
Iris Flowers
This well-known dataset includes measurements of the sepal length and width, petal length and width, and 150 iris flowers — 50 flowers from each of the three species. For classification and clustering issues, it is frequently utilized. The goal is to classify the flowers into one of three species. The models you should consider using are K-nearest neighbors (KNN), decision trees , Naive Bayes, neural networks, and support vector machines (SVMs), and the dataset can be found at the following link.
If you want to learn more about K-nearest neighbors (KNN), you can do so in the following article.
If you want to learn more about Support Vector Machines (SVM), follow the link bellow.
If you want to learn more about Bayes Classifiers, follow the link bellow.
Bayes Classifiers in depth
Bayes Theorem, Hypothesis, Probability, Machine Learning
ai.plainenglish.io
If you want to learn more about Neural Networks, follow the link bellow.
Or, more broadly, algorithm decision trees.
Titanic Survival
This dataset includes specific details about passengers who boarded the Titanic, including their age, the fare they paid, and whether or not they survived. The goal is to classify whether they survived or not. The models you should consider using are Logistic Regression, Decision Trees, Random Forests (to be posted), and Neural Networks. The dataset can be found at the following link.
If you want to learn more about Logistic Regression, you can check out this article.
Boston Housing
A dataset including details on Boston-area house prices, including the typical number of rooms per property, the level of crime, and the age of the building. The goal is to predict the house price. The models you should consider using are Linear Regression, Decision Trees, Random Forests, and Neural Networks. The dataset can be found at the following link.
If you want to learn more about Linear Regression, you can check out this article.
Adult Income
A dataset containing information on the income and demographic characteristics of individuals, commonly used for binary classification problems. The goal is to classify whether a person has an income of more than 50 thousand or less than and equal to 50 thousand. The models you should consider using are Logistic Regression, Decision Trees, Random Forests, and Neural Networks. The dataset can be found at the following link.
Digits
A dataset of images of handwritten digits, commonly used for image classification problems. The goal is to correctly classify the displayed picture numbers. The models you should consider using are K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Neural Networks, and Convolutional Neural Networks (CNNs) (to be posted). The dataset can be found at the following link.
CIFAR-10
A dataset of small images in 10 classes, often used for image classification and computer vision problems. The goal is to correctly classify the displayed picture of many objects. The models you should consider using are Neural Networks, Convolutional Neural Networks (CNNs), and Random Forests. The dataset can be found at the following link.
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