Introduction to Machine Learning

Welcome to a broad introduction to machine learning! Let's unravel the intriguing concepts of machine learning, deep learning, and artificial intelligence, and understand how these technologies are changing the world as we know it.

Machine Learning Defined

Machine learning develops computer programs that access data and use it to automatically learn and improve from experience. It empowers us to undertake complex tasks, from 3D printing houses to enabling personal assistants like Siri, Google Assistant, or Amazon Echo to handle everyday tasks. These smart assistants can play music, order food, voice control home appliances, request rides from Uber, and much more, all thanks to machine learning.

Understanding Artificial Intelligence, Machine Learning, and Deep Learning

Artificial Intelligence (AI) enables machines to mimic human behavior. Various technologies fall under AI, including IBM's Deep Blue chess, electronic game characters, and self-driving cars, to name a few.

Machine learning, a subset of AI, utilizes statistical methods, enabling machines to learn from their past data. This learning allows machines to leverage past inputs and answers to make better guesses in future attempts. Google's search algorithm and email spam filters are prime examples of machine learning applications.

Deep learning, another subset of machine learning, uses algorithms to train models to perform tasks. However, it's a black box model, meaning it's challenging for humans to track how these models make their predictions. Despite this, deep learning models can be highly effective at performing tasks.

 

Unpacking How Machine Learning Works

The process begins by training your data, which is then fed into a machine learning algorithm for processing. This data goes through another machine learning algorithm, after which it is tested using new data. Predictions are then checked, and if the results are unsatisfactory, the algorithm is retrained. This cycle of training, testing, and retraining continues until satisfactory results are obtained.

Exploring Different Types of Machine Learning

Machine learning can be broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning uses labelled data (where the output is already known) to train machine learning models. The models map the inputs to the outputs.

Unsupervised learning, on the other hand, uses unlabeled data. The model learns from the data, discovers patterns and features, and returns the output.

Reinforcement learning trains a machine to take appropriate actions and maximize reward in a particular situation. It employs a trial and error method to achieve the desired solution.

The selection of a learning algorithm depends on the problem at hand.

 

Prerequisites of Machine Learning

Four major prerequisites for machine learning include a basic understanding of computer science fundamentals and programming; intermediate statistical knowledge; comprehension of linear algebra and intermediate calculus; and, the ability to wrangle and clean data.

Applications of Machine Learning

Machine learning finds its applications in diverse fields, including object detection and instance segmentation, license plate detection, and automatic translation. Supervised learning techniques are generally used to solve classification and regression problems, while unsupervised learning is used for clustering and association problems. Reinforcement learning, being reward-based, is commonly used in the gaming industry, among other fields.

Machine learning is a vast, rapidly evolving field with limitless potential. In this introduction, we've merely skimmed the surface. Stay tuned for more insights, particularly into the fascinating world of Deep Learning, in our upcoming posts.

Keep exploring!
Prof. Reza Team