Machine learning is a type of artificial intelligence that allows computer systems to automatically learn and improve from experience without being explicitly programmed.
In machine learning, algorithms analyze data, identify patterns, and make predictions or decisions based on that data. The algorithms learn from the data they are trained on and use that knowledge to make accurate predictions or decisions on new, unseen data.
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning involves training a model on a labeled dataset, where the desired output is known. The model learns to map input data to the correct output based on the labeled examples.
Unsupervised learning involves training a model on an unlabeled dataset, where the desired output is unknown. The model learns to identify patterns and relationships within the data without any explicit guidance.
Reinforcement learning involves training a model to make decisions based on feedback from its environment. The model receives rewards or punishments for its actions and learns to maximize its rewards over time.
Machine learning has many applications in a variety of fields, including image recognition, natural language processing, speech recognition, and predictive modeling.
Welcome to the AI-ML-DS-Freshman-Starter-Kit! This repository is designed to help beginners in the fields of Machine Learning (ML), Data Science (DS), and Artificial Intelligence (AI) kickstart their learning journey. I have compiled a curated list of resources, tutorials, and projects to guide you through the basics and provide hands-on experience in these exciting fields. Hands-On Projects: Develop your skills and understanding by working on beginner-friendly projects, covering topics such as data visualization, regression analysis, classification, natural language processing, and more.