There are numerous machine learning algorithms implemented in Python, thanks to libraries like scikit-learn, TensorFlow, PyTorch, and others. Here's an overview of some common machine learning algorithms you can implement in Python:
Linear Regression: Used for modeling the relationship between a dependent variable and one or more independent variables.
Logistic Regression: A classification algorithm used to model the probability of a binary outcome.
Decision Trees: A versatile algorithm for classification and regression tasks. Decision trees split the data into subsets based on the most significant attribute. (Machine Learning Training in Pune)
Random Forest: An ensemble learning method that constructs multiple decision trees during training and outputs the mode of the classes or mean prediction of the individual trees.
Support Vector Machines (SVM): A powerful supervised learning algorithm used for classification tasks. It finds the hyperplane that best divides a dataset into classes.
K-Nearest Neighbors (KNN): A simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions).
Naive Bayes: A family of simple probabilistic classifiers based on applying Bayes' theorem with strong independence assumptions between the features.
K-Means Clustering: An unsupervised learning algorithm used to partition a dataset into clusters of similar data points.
Principal Component Analysis (PCA): A technique used for dimensionality reduction. It transforms the original features into a lower-dimensional space while preserving most of the variance in the data.
Gradient Boosting Machines (GBM): An ensemble technique that builds models sequentially, each new model correcting errors made by the previous ones. (Machine Learning Course in Pune)
Neural Networks: Deep learning models implemented using libraries like TensorFlow or PyTorch. They consist of interconnected layers of nodes, capable of learning complex patterns and relationships in data.
These are just some of the most common machine learning algorithms available in Python. Depending on your specific problem and data, you may need to explore and experiment with different algorithms to find the one that works best for your task.
