INSIDE ALAN TURING: QUOTES & CONTEMPLATIONS

* 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 Clarity: 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 Along: 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 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