Discover the basics of Machine Learning with our beginner-friendly guide. Dive into basic concepts, algorithms, and real-world applications, paving the way for data-driven insights and innovation
Features and labels are crucial in machine learning. To ensure algorithms can process data, categories must be converted into numerical formats using techniques like one-hot encoding and ordinal encoding. This post provides an overview of both methods, explaining their workings and applications
One-Hot and Ordinal Encoding for Features and Labels Read More »
Two fundamental building blocks of machine learning are features (input) and labels (output). This article explains what features and labels are, their different types, and how they are applied in various machine learning models.
Key Building Blocks of Machine Learning – Features and Labels Read More »
Welcome to the first post of our series on understanding Convolution Neural Networks. In this series we’ll explore the inner workings of CNNs, starting from the basics and gradually delving into advanced concepts.
What is Convolution In Convolution Neural Network (CNN)? Read More »
Artificial intelligence (AI) has evolved from a theoretical concept to a transformative technology impacting various industries. This article guides you through the origins of AI, its initial interpretations, and its progression into machine learning (ML) and deep learning (DL). By understanding these foundational concepts, you’ll be well-prepared to embark on practical projects and delve deeper into the dynamic world of AI and ML. Join me as we unravel the basics and set the stage for hands-on exploration in the fascinating field of artificial intelligence.
Basics of Artificial Intelligence: Concept, Brief History, Components Read More »
Discover the evolving world of machine learning methods in this article! From supervised to weakly supervised, weakly semi-supervised, and semi-supervised learning, uncover the unique benefits and applications of each approach. Whether you’re just starting out or seeking to deepen your understanding, explore how these methods work and when to apply them. Let’s dive in and unlock the potential of machine learning together!
Exploring Different Learning Approaches in Computer Vision Read More »
Learn how machines understand images like us and explore different image tasks, from recognizing objects to understanding scenes.
Exploring Different Image Tasks For Your Next Project Read More »
In this post, we’ll explore the fundamental concepts of dataset splitting in machine learning. We’ll cover the definitions of train, validation, and test sets, the importance of splitting the dataset, different partitioning strategies, and tips for ensuring proper dataset splitting. Join us as we unravel the keys to effective model development and evaluation.
How To Choose Train Validation and Test Sets For Your Model? Read More »
In this post, we’ll explore how to prevent overfitting in your machine learning models using simple regularization techniques. Dive into controlling model complexity and improving generalization for better performance in real-world situations.
Regularization Techniques to Prevent Model Overfitting Read More »
Overfitting, underfitting, and a model’s capacity are critical concepts in deep learning, particularly in the context of training neural networks.
Overfitting, Underfitting and Model’s Capacity in Deep Learning Read More »
