Machine Learning Algorithms

Jaiinfoway Helps build Machine learning Models for Startups. Some of Models what we build are mentioned in blocks

Machine learning models helps us in many tasks

• Object Recognition
• Summarization
• Prediction
• Classification
• Clustering
• Recommender systems

Regression (Prediction)

Linear RegressionPolynomial RegressionExponential RegressionLogistic RegressionLogarithmic Regression

Classification

• K-Nearest Neighbors
• Decision Trees
• Random Forest
• Support Vector Machine
• Naive Bayes

Clustering

• K-means
• DBSCAN
• Mean Shift
• Hierarchical

Association

Association algorithms is for associating co-occurring items or events Apriori

Anomaly Detection

Anomaly detection is for discovering abnormal activities and unusual cases like fraud detection

Sequence Pattern Mining

Linear RegressionPolynomial RegressionExponential RegressionLogistic RegressionLogarithmic Regression

Machine learning algorithms are classified into below groups

Supervised Learning algorithms

Supervised learning is a branch of machine learning(perhaps it is the mainstream of machine/deep learning for now) related to inferring a function from labeled training data. Training data consists of a set of *(input, target)* pairs, where the input could be a vector of features, and the target instructs what we desire for the function to output. Depending on the type of the *target*, we can roughly divide supervised learning into two categories: classification and regression. Classification involves categorical targets; examples ranging from some simple cases, such as image classification, to some advanced topics, such as machine translations and image caption. Regression involves continuous targets. Its applications include stock prediction, image masking, and others- which all fall in this category.

Unsupervised Learning algorithms

Unsupervised learning infers from unlabeled data, a function that describes hidden structures in data.Perhaps the most basic type of unsupervised learning is dimension reduction methods, such as PCA, t-SNE, while PCA is generally used in data preprocessing, and t-SNE usually used in data visualization.A more advanced branch is clustering, which explores the hidden patterns in data and then makes predictions on them; examples include K-mean clustering, Gaussian mixture models, hidden Markov models, and others.Along with the renaissance of deep learning, unsupervised learning gains more and more attention because it frees us from manually labeling data.

Artificial Intelligence, Machine Learning, and Deep Learning

by Jaiinfoway

Artificial intelligence (AI), as defined by Professor Andrew Moore, is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence

Machine learning refers to a scientific branch of AI, which focuses on the study of computer algorithms that allow computer programs to automatically improve through experience

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Deep learning is a subset of machine learning in which layered neural networks, combined with high computing power and large datasets, can create powerful machine learning models

Jaiinfoway is an ML Machine Learning first approach company which helps startup’s to leverage build Products leveraging Learning by Default