What do you think?
Rate this book
A Primer to the 42 Most commonly used Machine Learning Algorithms (With Code Samples).
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
• 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
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
• 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
192 pages, Paperback
Published January 27, 2023
2 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 of 1 review
June 27, 2023
This book would be probably useful for people with many years of practical experience in machine learning, as a quick recap of the most popular algorithms.
It does not explain the different algorithms themselves, which are also listed in alphabetical order instead of being presented in order of incremental complexity, organised per typical usage or with a didactical objective.
Unfortunately not a very useful book.
It does not explain the different algorithms themselves, which are also listed in alphabetical order instead of being presented in order of incremental complexity, organised per typical usage or with a didactical objective.
Unfortunately not a very useful book.
Displaying 1 of 1 review