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Artificial Intelligence & Machine Learning: artificial intelligence versus machine learning
About this Book
Get ready to use Python to explore the world of machine learning (ML)! You should take this book if you want to develop your career in data science or if you want to learn more about machine learning and deep learning. A moderate introduction to machine learning and what it is will be given at the start of this book.
Topics covered will include supervised vs. unsupervised learning, linear vs. non-linear regression, simple regression, and more. Then, using various classification methods, such as K-Nearest Neighbors (KNN), decision trees, and logistic regression, you will delve into categorization strategies. Additionally, you'll discover the value of clustering and its various forms, including DBSCAN, hierarchical clustering, and k-means. There will be a strong focus on practical learning in addition to the various topics you will master.
Learning Give instances of machine learning in different fields.Describe the procedures that machine learning follows to resolve issues.Give illustrations of several machine learning approaches.Describe the machine learning Python libraries.Describe how supervised and unsupervised algorithms differ from one another.Describe the features that different algorithms have.Show that you comprehend the fundamentals of regression.Show that you grasp basic linear regression.Give examples of evaluation methods for regression models.Describe the metrics used to assess the reliability of regression models.Show that you are familiar with multiple linear regression.Show that you are familiar with non-linear regression.To estimate a dataset, use simple and multiple linear regression.Compare and contrast the traits of the various classification techniques.Describe the K Nearest Neighbors algorithm's use.Describe the metrics for model evaluation.Describe the operation of a decision tree.Define the decision-tree construction process.Describe the powers of logistic regression.contrasting logistic regression with linear regression.Describe the process for altering a logistic regression model's parameters.Describe the logistic regression cost function and gradient descent.Describe the Support Vector Machine approach in general.Using different datasets and classification techniques, tackle real-world issues.Describe the various clustering techniques and the scenarios in which they are used.Explanation of the K-Means Clustering method.Describe the K-Means Clustering technique's accuracy issues.What is the purpose of hierarchical clustering?Describe the agglomerative algorithm for hierarchical clustering in general terms.Give a list of the benefits and drawbacks of employing hierarchical clustering.Describe the DBSCAN density-based clustering's capabilities.Apply clustering to various dataset types.Describe the operation of the various recommender systems.Give some examples of the benefits of employing recommendation systems.Describe the differences between recommender system implementations based on models and those that use memory.
Get ready to use Python to explore the world of machine learning (ML)! You should take this book if you want to develop your career in data science or if you want to learn more about machine learning and deep learning. A moderate introduction to machine learning and what it is will be given at the start of this book.
Topics covered will include supervised vs. unsupervised learning, linear vs. non-linear regression, simple regression, and more. Then, using various classification methods, such as K-Nearest Neighbors (KNN), decision trees, and logistic regression, you will delve into categorization strategies. Additionally, you'll discover the value of clustering and its various forms, including DBSCAN, hierarchical clustering, and k-means. There will be a strong focus on practical learning in addition to the various topics you will master.
Learning Give instances of machine learning in different fields.Describe the procedures that machine learning follows to resolve issues.Give illustrations of several machine learning approaches.Describe the machine learning Python libraries.Describe how supervised and unsupervised algorithms differ from one another.Describe the features that different algorithms have.Show that you comprehend the fundamentals of regression.Show that you grasp basic linear regression.Give examples of evaluation methods for regression models.Describe the metrics used to assess the reliability of regression models.Show that you are familiar with multiple linear regression.Show that you are familiar with non-linear regression.To estimate a dataset, use simple and multiple linear regression.Compare and contrast the traits of the various classification techniques.Describe the K Nearest Neighbors algorithm's use.Describe the metrics for model evaluation.Describe the operation of a decision tree.Define the decision-tree construction process.Describe the powers of logistic regression.contrasting logistic regression with linear regression.Describe the process for altering a logistic regression model's parameters.Describe the logistic regression cost function and gradient descent.Describe the Support Vector Machine approach in general.Using different datasets and classification techniques, tackle real-world issues.Describe the various clustering techniques and the scenarios in which they are used.Explanation of the K-Means Clustering method.Describe the K-Means Clustering technique's accuracy issues.What is the purpose of hierarchical clustering?Describe the agglomerative algorithm for hierarchical clustering in general terms.Give a list of the benefits and drawbacks of employing hierarchical clustering.Describe the DBSCAN density-based clustering's capabilities.Apply clustering to various dataset types.Describe the operation of the various recommender systems.Give some examples of the benefits of employing recommendation systems.Describe the differences between recommender system implementations based on models and those that use memory.
60 pages, Kindle Edition
Published January 7, 2023
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