MACHINE LEARNING: UNDERSTANDING THE FUNDAMENTALS OF AI TECHNOLOGY

It is difficult to give a precise definition of artificial intelligence. Some artificial intelligence scientists have attempted to define artificial intelligence (in short AI) in various ways. The automation of activities that we associates with human thinking, activities such as decision making, problem solving, learning definition by Richard and A field of study that seeks to explain and emulate intelligent behavior in terms of computational process by Robert.

ARTIFICIAL INTELLIGENCE

Science that empowers computers to mimic human intelligence such as decision making, text processing, and visual perception. Ai is a broader field (i.e.: the big umbrella) that contains several subfield such as machine learning, robotics, and computer vision.

MACHINE LEARNING:

Machine Learning is a part of Artificial Intelligence that helps machines get better at doing things by learning from experience. It's worth noting that while all machine learning techniques are considered AI, not everything in AI is Machine Learning. For instance, some basic rule-based systems are called AI, but they don't actually learn from experience, so they're not considered part of machine learning.

TYPES OF MACHINE LEARNING:

SUPERVISED LEARNING: Supervised learning is a type of machine learning technique uses the labeled datasets to train algorithms to predict outcomes and recognize the pattern.

SUPERVISED LEARNING ALGORITHMS:

LINEAR REGRESSION:

Linear regression is a simple and commonly used supervised learning algorithm for predicting a continuous target variable based on one or more input features. It models the relationship between the input variables and the target variable as a linear equation.

LOGISTIC REGRESSION:

Logistic regression is used for binary classification tasks, where the target variable has two possible outcomes. It models the probability that a given input belongs to a particular class using a logistic function.

DECISION TREE:

Decision tree are versatile supervised learning algorithm that can perform both classification and regression tasks. They partition the feature space into regions and make predictions based on the majority class or average value of the training instances within each region.

RANDOM FOREST:

Random forest is ensemble learning method that combine multiple decision trees to improve predictive performance. They train a collection of decision trees on random subsets of the data and combine their predictions through voting or averaging.

SUPPORT VECTOR MACHINES (SVM):

SVMs are powerful supervised learning algorithms for classification tasks, particularly in high-dimensional feature spaces. They find the optimal hyperplane that separates the data into different classes with the maximum margin.

UNSUPERVISED LEARNING:

Unsupervised learning is a type of machine learning that learns from data without human supervision. Unsupervised learning models are given unlabeled data and allowed to discover the patterns and insights.

UNSUPERVISED LEARNING ALGORITHMS:

K-MEANS CLUSTERING:

K-means clustering is a popular algorithm for partitioning a dataset into k distinct, non-overlapping clusters. It aims to minimize the within-cluster variance, assigning each data point to the cluster with the nearest centroid.

HIERARCHICAL CLUSTERING:

Hierarchical clustering builds a hierarchy of clusters by recursively merging or splitting clusters based on their similarity. It can produce dendrograms that visually represent the clustering structure of the data.

REINFORCEMENT LEARNING:

Reinforcement learning is a type of machine learning where software learns to make decisions in order to get the best possible outcome. It works by trying different actions and learning from the results, much like how humans learn through trial and error to reach their goals.

CONCLUSION:

In conclusion, artificial intelligence (AI) is changing the way we do things. It's about teaching computers to learn from experience, like how humans learn. Machine learning is one part of AI. It helps computers make predictions and find patterns in data. There are different types of machine learning, like supervised learning where computers learn from labeled data, and unsupervised learning where they find patterns in data without labels. Reinforcement learning is another type where computers learn by trial and error. AI has big potential to improve our lives in many ways, like in healthcare and transportation. As it keeps growing, AI could bring even more exciting changes and advancements in the future.