Machine Learning : Master Supervised and Unsupervised Learning Algorithms with Real Examples by Dr Ruchi Doshi, Dr Kamal Kant Hiran
Requirements: .ePUB reader, 3 MB
Overview: The book offers the readers the fundamental concepts of Machine Learning techniques in a user-friendly language. The book aims to give in-depth knowledge of the different Machine Learning (ML) algorithms and the practical implementation of the various ML approaches.
This book covers different Supervised Machine Learning algorithms such as Linear Regression Model, Naive Bayes classifier Decision Tree, K-nearest neighbor, Logistic Regression, Support Vector Machine, Random forest algorithms, Unsupervised Machine Learning algorithms such as k-means clustering, Hierarchical Clustering, Probabilistic clustering, Association rule mining, Apriori Algorithm, f-p growth algorithm, Gaussian mixture model and Reinforcement Learning algorithm such as Markov Decision Process (MDP), Bellman equations, policy evaluation using Monte Carlo, Policy iteration and Value iteration, Q-Learning, State-Action-Reward-State-Action (SARSA). It also includes various feature extraction and feature selection techniques, the Recommender System, and a brief overview of Deep Learning.
Genre: Non-Fiction > Tech & Devices

Download Instructions:
https://userupload.net/3xogr6xzr839
https://dropgalaxy.vip/9y4a00uml1ll
Trouble downloading? Read This.
Requirements: .ePUB reader, 3 MB
Overview: The book offers the readers the fundamental concepts of Machine Learning techniques in a user-friendly language. The book aims to give in-depth knowledge of the different Machine Learning (ML) algorithms and the practical implementation of the various ML approaches.
This book covers different Supervised Machine Learning algorithms such as Linear Regression Model, Naive Bayes classifier Decision Tree, K-nearest neighbor, Logistic Regression, Support Vector Machine, Random forest algorithms, Unsupervised Machine Learning algorithms such as k-means clustering, Hierarchical Clustering, Probabilistic clustering, Association rule mining, Apriori Algorithm, f-p growth algorithm, Gaussian mixture model and Reinforcement Learning algorithm such as Markov Decision Process (MDP), Bellman equations, policy evaluation using Monte Carlo, Policy iteration and Value iteration, Q-Learning, State-Action-Reward-State-Action (SARSA). It also includes various feature extraction and feature selection techniques, the Recommender System, and a brief overview of Deep Learning.
Genre: Non-Fiction > Tech & Devices
Download Instructions:
https://userupload.net/3xogr6xzr839
https://dropgalaxy.vip/9y4a00uml1ll
Trouble downloading? Read This.