Working and Implementation of various Machine learning classification algorithms using realtime dataset. Machine learning (ML) is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and models that allow computers to learn and make predictions or decisions without being explicitly programmed. Here's a short note about ML:
Machine learning involves the use of statistical techniques to enable computers to learn from and analyze data, identify patterns, and make predictions or take actions based on that learning.
ML algorithms can be categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled data to train models, unsupervised learning discovers patterns and relationships in unlabeled data, and reinforcement learning enables agents to learn through trial and error interactions with an environment.
ML models require training data, which consists of input examples and their corresponding correct outputs or labels. During the training process, the model adjusts its internal parameters to minimize the difference between its predicted outputs and the true labels. Once trained, ML models can be used to make predictions or decisions on new, unseen data. The performance of ML models is typically evaluated using various metrics, such as accuracy, precision, recall, and F1 score, depending on the specific task and problem domain.ML finds applications in numerous fields, including image and speech recognition, natural language processing, recommendation systems, autonomous vehicles, finance, healthcare, and many others. It has the potential to automate tasks, gain insights from large datasets, improve decision-making processes, and enable the development of intelligent systems.
Models that I practiced:
※ LOGISTIC REGRESSION
※ SVM (SUPPORT VECTOR MACHINE)
※ NAIVE BAYE'S CLASSIFIER
LOGISTIC REGRESSION :
It's a supervised ML algorithm majorly used for classification related problems. It works by classifying the datapoints by implementing a sigmoid curve. Its an "S" shaped curve.
SVM (SUPPORT VECTOR MACHINE):
It is also an supervised classification model.It works by vectors in the frame.It forms a hyperplane between vectors and classifies them into different classes. More complex problems would have higher dimensional space for classification.
NAIVE BAYE'S CLASSIFIER:
It is also an supervised classification model. It works based on probablity of words. These models are mostly used for classsifiying spam mails and sentimental analysis.There are three types of Naives Bayes algorithms.
i) Guassian
ii) Bernouili
iii) Mutinomial