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A Primer to the 42 Most commonly used Machine Learning Algorithms (With Code Samples).
* Unlock the World of Machine Learning!*
Dive into "A Primer to the 42 Most Commonly Used Machine Learning Algorithms (With Code Samples)" and embark on a journey that demystifies the complexities of machine learning. This insightful guide is your key to understanding the core algorithms that power the future of technology.Explore with Each chapter unravels a new algorithm, presenting it in a clear, concise manner. From decision trees to neural networks, get ready to explore the intricate world of machine learning with ease.Code Don't just learn, do. With practical code samples in Python, this book isn't just about understanding concepts; it's about applying them. Whether you're a beginner or looking to expand your skill set, these hands-on examples will solidify your knowledge."A Primer to the 42 Most Commonly Used Machine Learning Algorithms (With Code Samples)" is more than a book; it's a journey into the heart of AI. Grab your copy and start transforming your understanding of machine learning today!
About the Author
Murat Durmus is CEO and founder of AISOMA (a Frankfurt am Main (Germany) based company specializing in AI-based technology development and consulting) and Author of the book "Mindful AI - Reflections on Artificial Intelligence" and "INSIDE ALAN TURING"
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
Dive into "A Primer to the 42 Most Commonly Used Machine Learning Algorithms (With Code Samples)" and embark on a journey that demystifies the complexities of machine learning. This insightful guide is your key to understanding the core algorithms that power the future of technology.Explore with Each chapter unravels a new algorithm, presenting it in a clear, concise manner. From decision trees to neural networks, get ready to explore the intricate world of machine learning with ease.Code Don't just learn, do. With practical code samples in Python, this book isn't just about understanding concepts; it's about applying them. Whether you're a beginner or looking to expand your skill set, these hands-on examples will solidify your knowledge."A Primer to the 42 Most Commonly Used Machine Learning Algorithms (With Code Samples)" is more than a book; it's a journey into the heart of AI. Grab your copy and start transforming your understanding of machine learning today!
About the Author
Murat Durmus is CEO and founder of AISOMA (a Frankfurt am Main (Germany) based company specializing in AI-based technology development and consulting) and Author of the book "Mindful AI - Reflections on Artificial Intelligence" and "INSIDE ALAN TURING"
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
- GenresArtificial Intelligence
194 pages, Kindle Edition
Published January 26, 2023
14 people are currently reading
29 people want to read
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April 5, 2023I guess this is really a good book. So I will bookmark it for my future reading. As I really want to know how AI can be used in retail business that I am starting.
July 2, 2024
If you're planning to write a book with ChatGPT, have the decency to perform basic proofreading to ensure that sentences do not repeat within a paragraph and do not end abruptly.
If you’re interested in the topic, here is a shortcut for you to achieve better results:
1. Open ChatGPT.
2. Ask for the 42 most commonly used ML algorithms.
For each algorithm in the list:
1. Explain the algorithm in simple language.
2. Provide real-world examples.
3. Provide Python code for the algorithm.
If you’re interested in the topic, here is a shortcut for you to achieve better results:
1. Open ChatGPT.
2. Ask for the 42 most commonly used ML algorithms.
For each algorithm in the list:
1. Explain the algorithm in simple language.
2. Provide real-world examples.
3. Provide Python code for the algorithm.
April 5, 2023
That is a good idea. And the first thing I thought about was the idea of using machine learning like computer vision platform to be able to do the crowd count, average time spent in queues etc. I am sure these parameters are essential when we speak of retail business, for example. This solution can integrate all this.
Displaying 1 - 3 of 3 reviews