MACHINE LEARNING

Machine Learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches. Machine learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine, where it is too costly to develop algorithms to perform the needed tasks.

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can effectively generalize and thus perform tasks without explicit instructions. Recently, generative artificial neural networks have been able to surpass many previous approaches in performance. Machine learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture, and medicine, where it is too costly to develop algorithms to perform the needed tasks.

The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods. Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning.

Machine Learning

Unit	Details Contents	Unit	Details Contents
Unit -I	Statistics for ML ML with Statistics Basic Statistics Population & sample Central Tendency Skewness and Kurtosis Standard Normal Distribution Hypothesis Testing	Unit -II	Data Preprocessing in ML Data Preprocessing Importing Libraries Handling Missing data Encoding Categorical Data Split into training & test sets Feature scaling
Unit -III	Classification Classification Logistic Regression Classification K-NN Naive Bayes Classifier Decision Tree Support Vector Machine Random Forest K-Nearest Neighbors Regression Regression Linear Regression Polynomial Regression Neural Network Regression Support Vector Regression Decision Tree Regression Lasso Regression Ridge Regression	Unit -IV	Clustering Clustering K-Means Clustering Elbow Method Hierarchical Clustering Dendrograms Optimal Mean-shift Clustering DBSCAN Clustering Agglomerative Hierarchical Clustering Gaussian Mixture
Unit -V	Association Rule Learning Association Rule Learning Apriori algorithm ECLAT Algorithm	Unit -VI	Decision Making Reinforcement Learning MDP Learning Monte Carlo learning TD Learning Q-Learning Markov Decision Processes Multi-Armed Bandit Problem UCB Algorithm
Unit -VII	Natural Language Processing (NLP) Natural Language Processing Deep Learning Bag-of-Words Model	Unit -VIII	Deep Learning & Neural Networks Artificialt Neural Network Activation Functions Convolution Neural Network Gradient Descent Backpropagation
Unit -IX	Dimensionality Reduction Dimensionality Reduction Principal component analysis Linear Discriminant Analysis Kernel PCA	Unit -X	Model Selection & XGBoost Model Selection k-Fold Cross-Validation Bias–variance tradeoff XGBoost