A Primer to the 42 Most commonly used Machine Learning Algorithms

Whether you're a data scientist, software engineer, or simply interested in learning about machine learning, "A Primer to the 42 Most commonly used Machine Learning Algorithms (With Code Samples)" is an excellent resource for gaining a comprehensive understanding of this exciting field.

This book introduces you to the 42 most commonly used machine learning algorithms in an understandable way. Each algorithm is also demonstrated with a simple code example in Python.

The following algorithms are covered in this book:

• ADABOOST
• ADAM OPTIMIZATION
• AGGLOMERATIVE CLUSTERING
• ARMA/ARIMA MODEL
• BERT
• CONVOLUTIONAL NEURAL NETWORK
• DBSCAN
• DECISION TREE
• DEEP Q-LEARNING
• EFFICIENTNET
• FACTOR ANALYSIS OF CORRESPONDENCES
• GAN
• GMM
• GPT-3
• GRADIENT BOOSTING MACHINE
• GRADIENT DESCENT
• GRAPH NEURAL NETWORKS
• HIERARCHICAL CLUSTERING
• HIDDEN MARKOV MODEL (HMM)
• INDEPENDENT COMPONENT ANALYSIS
• ISOLATION FOREST
• K-MEANS
• K-NEAREST NEIGHBOUR
• LINEAR REGRESSION
• LOGISTIC REGRESSION
• LSTM
• MEAN SHIFT
• MOBILENET
• MONTE CARLO ALGORITHM
• MULTIMODAL PARALLEL NETWORK
• NAIVE BAYES CLASSIFIERS
• PROXIMAL POLICY OPTIMIZATION
• PRINCIPAL COMPONENT ANALYSIS
• Q-LEARNING
• RANDOM FORESTS
• RECURRENT NEURAL NETWORK
• RESNET
• SPATIAL TEMPORAL GRAPH CONVOLUTIONAL NETWORKS
• STOCHASTIC GRADIENT DESCENT
• SUPPORT VECTOR MACHINE
• WAVENET
• XGBOOST