Machine learning

It is a subfield of artificial intelligence (AI) that focuses on building algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed. It involves training algorithms on large data sets to recognize patterns and make decisions based on the learned data.

Our machine learning syllabus covers the following topics:

1. Introduction to machine learning:

 This section covers the basic concepts of machine learning, including supervised and unsupervised learning, regression and classification, overfitting and underfitting, and bias and variance.

1. Linear Regression:

 Linear regression is a statistical approach for modeling the relationship between a dependent variable and one or more independent variables. It is used to make predictions and to understand the relationship between variables. Topics covered may include simple linear regression, multiple linear regression, model selection, and regularization.

1. Logistic Regression: 

Logistic regression is used for binary classification problems, where the goal is to predict one of two possible outcomes. Topics covered may include logistic regression models, maximum likelihood estimation, and regularization.

1. Decision Trees:

 Decision trees are a type of model used for classification and regression. Topics covered may include tree-based models, tree pruning, and random forests.

1. Naive Bayes: 

Naive Bayes is a probabilistic classifier based on Bayes theorem. Topics covered may include the Naive Bayes algorithm, Gaussian Naive Bayes, and multinomial Naive Bayes.

1. K-Nearest Neighbors (KNN):

 KNN is a non-parametric method for classification and regression. Topics covered may include the KNN algorithm, distance metrics, and model selection.

1. Support Vector Machines (SVMs):

SVMs are a type of model used for binary classification. Topics covered may include linear SVMs, kernel SVMs, and model selection.

1. Neural Networks:

Neural networks are a type of model inspired by the structure of the human brain. Topics covered may include feedforward networks, convolutional neural networks, recurrent neural networks, and deep learning.

1. Clustering (K-Means, Hierarchical):

Clustering is a type of unsupervised learning used for grouping similar data points into clusters. Topics covered may include the K-Means algorithm, hierarchical clustering, and model selection.

1. Dimensionality Reduction (PCA, t-SNE):

Dimensionality reduction is a technique for reducing the number of features in a dataset while retaining important information. Topics covered may include PCA, t-SNE, and other dimensionality reduction methods.

1. Ensemble Methods (Random Forest, Gradient Boosting):

 Ensemble methods are techniques for combining the predictions of multiple models to improve the overall accuracy of predictions. Topics covered may include random forests, gradient boosting, and model selection.

1. Model evaluation and selection:

This section covers techniques for evaluating the performance of machine learning models and selecting the best model for a given problem. Topics may include model selection, cross-validation, and performance metrics.

1. Feature Engineering:

 Feature engineering is the process of creating new features from existing data that can improve the performance of machine learning models. Topics covered may include feature extraction, feature scaling, and feature selection.

1. Overfitting and Regularization:

Overfitting is a common problem in machine learning where a model is too complex and performs well on training data but poorly on test data. Topics covered may include regularization, model selection, and ensemble methods.

1. Reinforcement learning

 Reinforcement learning is a type of machine learning where an agent learns to make decisions in an environment by performing actions and receiving rewards. Topics covered may include Markov decision processes, Q-learning, and deep reinforcement learning.

1. Unsupervised learning: 

Unsupervised learning is a type of machine learning used for finding patterns and relationships in data without labeled outcomes. Topics covered may include clustering, dimensionality reduction, and anomaly detection