By leveraging machine learning algorithms, companies can analyze vast amounts of data in real-time, identifying patterns and trends that were previously unimaginable. This enables organizations to optimize their processes, identify areas for improvement, and develop more accurate sales forecasts.
What is Machine Learning?
Machine Learning is a science of programming machines to think and act like humans without being specifically programmed. We already use machine learning in our daily life without knowing it. For instance; e-mail spam recognition, spell check, Youtube video recommendation are implemented using machine learning. Machine learning uses algorithms to learn tasks, these algorithms are fed with data from which they learn to perform these tasks. This means that over time as changes in data occur we don't need to reprogram our application. Just let it find patterns and learn from the new data.
“ Machine Learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data. “
What Kind of ML Techniques Commonly Used?
There are three types of technique when we apply Machine Learning algorithms. These are;
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
Supervised learning is a machine learning approach that’s defined by its use of labeled datasets. These datasets are designed to train or 'supervise' algorithms into classifying data or predicting outcomes accurately. Using labeled inputs and outputs, the model can measure its accuracy and learn over time. Supervised learning can be separated into two types of problems when data mining: classification and regression. Most commonly used supervised learning algorithms are Linear regression, Logistic regression, K-nearest neighbor, Random forest, Decision Trees and Naive bayes.
Unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns in data without the need for human intervention. Unsupervised learning models are used for three main tasks: Clustering, association and dimensionality reduction. Most commonly used unsupervised learning algorithms are; Fuzzy means, Singular value decomposition, K-means clustering, Apriori, Hierarchical clustering and Principal component analysis.
Reinforcement learning is a machine learning training method based on rewarding desired behaviors and punishing undesired ones. In general, a reinforcement learning agent -- the entity being trained -- is able to perceive and interpret its environment, take actions and learn through trial and error. Reinforcement learning is one of several approaches developers use to train machine learning systems. What makes this approach important is that it empowers an agent, whether it's a feature in a video game or a robot in an industrial setting, to learn to navigate the complexities of the environment it was created for. Over time, through a feedback system that typically includes rewards and punishments, the agent learns from its environment and optimizes its behaviors.