AI strives to create machines capable of performing tasks requiring human intelligence, such as problem-solving, learning, and decision-making. Machine Learning is currently the most effective approach for understanding AI. The complexity of human data is easier for a machine to process and utilize when it is already trained. Now that training part is the essential requirement in Machine Learning.
Datasets are collections of data points used to train and evaluate machine learning models. Structured like tables, rows represent individual data points, and columns define their features or attributes. High-quality datasets are essential for building effective AI systems. They are basically large amounts of data.
A subfield of AI, ML algorithms enable computers to learn patterns and relationships from data without explicit programming. Data analysis drives prediction and decision-making capabilities in ML models.
A specialized area within ML, Deep Learning utilizes deep neural networks (multiple layers) to extract complex patterns and representations from extensive datasets. DL's strength lies in its ability to discern intricate features, enabling more nuanced insights and highly accurate predictions.
In DL, fine-tuning adapts pre-trained models (already trained on massive datasets) to specific tasks by training them further on smaller, targeted datasets. This technique accelerates the model development process and efficiently applies learned knowledge to new scenarios.