Your cart is currently empty!
Tag: Statistics
Python for Probability, Statistics, and Machine Learning
Price:$99.99– $19.85
(as of Dec 25,2024 11:47:47 UTC – Details)
Publisher : Springer; 2nd ed. 2019 edition (July 11, 2019)
Language : English
Hardcover : 398 pages
ISBN-10 : 3030185443
ISBN-13 : 978-3030185442
Item Weight : 1.55 pounds
Dimensions : 6.25 x 1.25 x 9.5 inches
Python is a powerful programming language that is widely used in the fields of probability, statistics, and machine learning. Its simplicity, readability, and vast array of libraries make it a popular choice for data analysis and modeling.In probability and statistics, Python provides libraries such as NumPy, SciPy, and pandas that offer a wide range of functions for data manipulation, statistical analysis, and probability calculations. These libraries make it easy to perform tasks such as hypothesis testing, regression analysis, and data visualization.
For machine learning, Python offers libraries like scikit-learn, TensorFlow, and Keras that provide tools for building and training machine learning models. These libraries make it easy to implement algorithms such as linear regression, support vector machines, and neural networks.
Whether you are a student, researcher, or data scientist, Python is a valuable tool for working with probability, statistics, and machine learning. Its versatility and ease of use make it a go-to language for analyzing data and building predictive models.
So if you are looking to enhance your skills in probability, statistics, and machine learning, consider learning Python and exploring its vast array of libraries and tools. It will open up a world of possibilities for analyzing data and making informed decisions.
#Python #Probability #Statistics #Machine #Learning
Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics
Price:$65.99– $42.99
(as of Dec 24,2024 20:46:27 UTC – Details)
From the brand
Explore more Data Science
Sharing the knowledge of experts
O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.
Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.
Publisher : O’Reilly Media; 1st edition (July 5, 2022)
Language : English
Paperback : 349 pages
ISBN-10 : 1098102932
ISBN-13 : 978-1098102937
Item Weight : 1.28 pounds
Dimensions : 7 x 0.75 x 9 inches
Are you looking to take your data science skills to the next level? In order to truly excel in this field, it’s essential to have a strong foundation in math. From understanding complex algorithms to interpreting data accurately, math plays a crucial role in every aspect of data science.One of the key areas of math that data scientists must master is linear algebra. This branch of mathematics deals with vectors, matrices, and systems of linear equations, all of which are essential for performing operations on large datasets. By understanding linear algebra, you’ll be able to manipulate and analyze data more efficiently, leading to more accurate insights and predictions.
Probability and statistics are also vital components of data science. Probability theory allows data scientists to quantify uncertainty and make informed decisions based on the likelihood of certain events occurring. Statistics, on the other hand, helps data scientists make sense of data by analyzing and interpreting patterns and trends. By mastering probability and statistics, you’ll be able to draw meaningful conclusions from your data and make more informed decisions.
By taking control of your data with fundamental linear algebra, probability, and statistics, you’ll be better equipped to tackle complex data science problems and unlock valuable insights. So if you’re serious about excelling in the field of data science, make sure to brush up on your math skills and take your data analysis to the next level.
#Essential #Math #Data #Science #Control #Data #Fundamental #Linear #Algebra #Probability #Statistics
Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems
Price: $72.18
(as of Dec 24,2024 20:37:48 UTC – Details)
Publisher : Morgan Kaufmann; 1st edition (October 15, 1990)
Language : English
Hardcover : 223 pages
ISBN-10 : 1558600655
ISBN-13 : 978-1558600652
Item Weight : 1.19 pounds
Dimensions : 6.5 x 1.25 x 9 inches
In today’s rapidly evolving technological landscape, the concept of computer systems that learn has become increasingly important. From self-driving cars to personalized recommendations on streaming platforms, these systems are revolutionizing the way we interact with technology. But how do they actually work?In this post, we will explore the various classification and prediction methods that are commonly used in the development of these intelligent systems. Drawing from fields such as statistics, neural nets, machine learning, and expert systems, we will delve into the algorithms and techniques that enable computers to learn from data and make informed decisions.
Statistics plays a crucial role in the development of these systems, providing the foundation for many machine learning algorithms. By analyzing and interpreting data, statisticians can uncover patterns and trends that help computers make sense of the world around them. Techniques such as regression analysis, clustering, and hypothesis testing are commonly used to classify and predict outcomes based on historical data.
Neural nets, inspired by the structure of the human brain, are another key component of computer systems that learn. These interconnected networks of nodes are capable of processing vast amounts of data and identifying complex patterns. Through techniques such as backpropagation and gradient descent, neural nets can be trained to recognize images, understand natural language, and even play games at a superhuman level.
Machine learning algorithms, which encompass a wide range of techniques including decision trees, support vector machines, and deep learning, are at the forefront of the development of intelligent systems. By feeding large amounts of training data into these algorithms, computers can learn to make predictions and classify new data with a high degree of accuracy. These algorithms are used in a wide range of applications, from fraud detection to medical diagnosis.
Expert systems, on the other hand, rely on human knowledge and expertise to make decisions. By encoding the rules and heuristics used by experts in a specific domain, these systems can provide recommendations and insights based on their knowledge base. While they may not be as flexible or adaptable as machine learning algorithms, expert systems are still widely used in fields such as healthcare, finance, and engineering.
In conclusion, computer systems that learn are a powerful tool for unlocking the potential of data and making informed decisions in a wide range of applications. By combining techniques from statistics, neural nets, machine learning, and expert systems, developers can create intelligent systems that can adapt and evolve in response to new challenges and opportunities. As these technologies continue to advance, the possibilities for innovation and discovery are truly limitless.
#Computer #Systems #Learn #Classification #Prediction #Methods #Statistics #Neural #Nets #Machine #Learning #Expert #Systems
Pattern Recognition and Machine Learning (Information Science and Statistics)
Price:$84.99– $49.45
(as of Dec 24,2024 18:27:54 UTC – Details)
Publisher : Springer; 2006th edition (August 23, 2016)
Language : English
Paperback : 798 pages
ISBN-10 : 1493938436
ISBN-13 : 978-1493938438
Item Weight : 2.31 pounds
Dimensions : 6.9 x 1.7 x 9.8 inches
Pattern Recognition and Machine Learning: Exploring the Intersection of Information Science and StatisticsIn the ever-evolving field of information science and statistics, the study of pattern recognition and machine learning has emerged as a powerful tool for analyzing and interpreting complex data sets. By leveraging algorithms and computational techniques, researchers and practitioners are able to uncover hidden patterns, trends, and relationships within vast amounts of information.
In this post, we will delve into the key concepts and principles that underpin pattern recognition and machine learning, exploring how they are used to extract valuable insights from data. We will also discuss the various applications of these techniques across a range of industries, from healthcare and finance to marketing and cybersecurity.
Join us as we unravel the intricate world of pattern recognition and machine learning, and discover how these cutting-edge methodologies are reshaping the landscape of information science and statistics.
#Pattern #Recognition #Machine #Learning #Information #Science #Statistics
The Data Detective: Ten Easy Rules to Make Sense of Statistics
Price:$20.00– $12.59
(as of Dec 24,2024 16:44:02 UTC – Details)
Publisher : Riverhead Books (February 1, 2022)
Language : English
Paperback : 336 pages
ISBN-10 : 0593084667
ISBN-13 : 978-0593084663
Item Weight : 2.31 pounds
Dimensions : 5.5 x 0.9 x 8.2 inchesCustomers say
Customers find the book provides insightful and clear explanations of critical topics. They describe it as an easy, well-written read that is enjoyable and worthwhile. However, some feel the content lacks depth and is overly simplistic.
AI-generated from the text of customer reviews
Are you feeling overwhelmed by all the statistics and data around you? Don’t worry, I’ve got you covered! As a self-proclaimed Data Detective, I’ve compiled ten easy rules to help you make sense of all that information. Whether you’re a student, a professional, or just someone who wants to better understand the world around you, these rules will help you navigate the world of statistics with confidence. So grab a notebook and pen, and let’s get started on our journey to becoming data-savvy detectives!Rule #1: Question Everything
Don’t take statistics at face value. Always ask yourself where the data came from, how it was collected, and who is interpreting it. Remember, not all statistics are created equal.Rule #2: Look for Trends
Instead of focusing on individual data points, look for patterns and trends over time. This will help you see the bigger picture and make more informed decisions.Rule #3: Check the Sample Size
A small sample size can lead to misleading results. Make sure the data you’re looking at is based on a representative sample to avoid any misinterpretations.Rule #4: Consider the Source
Not all sources of data are reliable. Be wary of bias and conflicts of interest when evaluating statistics from different organizations or individuals.Rule #5: Understand the Methodology
How was the data collected? What statistical methods were used to analyze it? Understanding the methodology behind the statistics will help you better interpret the results.Rule #6: Don’t Confuse Correlation with Causation
Just because two variables are correlated doesn’t mean that one causes the other. Be cautious when making causal claims based on statistical relationships.Rule #7: Use Visualizations
Charts, graphs, and other visualizations can help make complex data more digestible. Visual representations of data can also reveal patterns and trends that may not be immediately apparent.Rule #8: Check for Outliers
Outliers can skew the results of statistical analyses. Make sure to identify and investigate any outliers in the data before drawing conclusions.Rule #9: Consult Experts
If you’re feeling overwhelmed or unsure about a particular set of statistics, don’t hesitate to consult with experts in the field. They can provide valuable insights and help you make sense of the data.Rule #10: Keep Learning
Statistics is a complex and ever-evolving field. Stay curious, keep learning, and don’t be afraid to dive deeper into the world of data analysis.By following these ten easy rules, you’ll be well on your way to becoming a Data Detective extraordinaire. So go forth, armed with your newfound statistical savvy, and unravel the mysteries of the data world!
#Data #Detective #Ten #Easy #Rules #Sense #Statistics
Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R
Price: $49.39
(as of Dec 24,2024 15:11:52 UTC – Details)
ASIN : B071YW2RKP
Publisher : Packt Publishing; 1st edition (July 21, 2017)
Publication date : July 21, 2017
Language : English
File size : 23923 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 680 pages
Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and RIn the world of machine learning, understanding statistics is crucial for building accurate and efficient models. Whether you are working with supervised, unsupervised, or reinforcement learning algorithms, having a solid grasp of statistical concepts can help you analyze data, make informed decisions, and improve the performance of your models.
In this post, we will explore various statistical techniques that can be applied to machine learning models using popular programming languages like Python and R. From understanding basic statistical concepts to implementing advanced statistical methods, we will cover everything you need to know to enhance your machine learning skills.
Supervised Learning:
– Regression analysis: Learn how to use regression analysis to predict continuous outcomes and evaluate the relationship between variables.
– Classification techniques: Explore different classification algorithms like logistic regression, decision trees, random forests, and support vector machines.
– Model evaluation: Understand how to evaluate the performance of your models using metrics like accuracy, precision, recall, and F1 score.Unsupervised Learning:
– Clustering: Discover how clustering algorithms like K-means, hierarchical clustering, and DBSCAN can be used to group similar data points together.
– Dimensionality reduction: Learn about techniques like principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) to reduce the dimensionality of your data.Reinforcement Learning:
– Markov decision processes: Understand the basics of Markov decision processes and how they can be used to model sequential decision-making processes.
– Q-learning: Explore the Q-learning algorithm and learn how to use it to train agents to make optimal decisions in a given environment.Throughout this post, we will provide code examples in Python and R to demonstrate how these statistical techniques can be implemented in practice. By the end of this post, you will have a solid understanding of how statistics can be leveraged to improve the performance of your machine learning models across different domains and applications. Stay tuned for more insights and practical tips on mastering statistics for machine learning!
#Statistics #Machine #Learning #Techniques #exploring #supervised #unsupervised #reinforcement #learning #models #Python
Fundamentals of Supervised Machine Learning: With Applications in Python, R, and Stata (Statistics and Computing)
Price: $99.99
(as of Dec 24,2024 10:51:56 UTC – Details)
Publisher : Springer (November 15, 2024)
Language : English
Paperback : 420 pages
ISBN-10 : 3031413393
ISBN-13 : 978-3031413391
Item Weight : 1.56 pounds
Dimensions : 6.1 x 0.87 x 9.25 inches
In this post, we will delve into the fundamentals of supervised machine learning, exploring its key concepts and methodologies. We will also discuss how these techniques can be applied using popular programming languages such as Python, R, and Stata.Supervised machine learning is a type of machine learning where the algorithm is trained on a labeled dataset, meaning that the input data is paired with the correct output. The goal of supervised learning is to learn a mapping from input to output, so that the algorithm can make predictions on new, unseen data.
Some of the key concepts in supervised machine learning include:
1. Training and testing datasets: In supervised learning, the dataset is typically divided into a training set and a testing set. The training set is used to train the model, while the testing set is used to evaluate its performance.
2. Classification vs. regression: Supervised learning tasks can be categorized into two main types – classification and regression. In classification, the goal is to predict a categorical variable, while in regression, the goal is to predict a continuous variable.
3. Model evaluation: There are various metrics that can be used to evaluate the performance of a supervised learning model, such as accuracy, precision, recall, and F1 score.
Now, let’s discuss how these concepts can be applied in popular programming languages such as Python, R, and Stata.
Python: Python is one of the most popular programming languages for machine learning, with libraries such as scikit-learn and TensorFlow that make it easy to implement supervised learning algorithms. In Python, you can easily load and preprocess data, train machine learning models, and evaluate their performance.
R: R is another popular programming language for statistical computing and data analysis, with packages such as caret and randomForest that are commonly used for supervised machine learning. In R, you can perform tasks such as data visualization, feature selection, model training, and evaluation.
Stata: Stata is a statistical software package that is commonly used in social sciences research. While Stata may not have as many machine learning libraries as Python or R, it still has tools for performing supervised learning tasks such as linear regression and logistic regression.
In conclusion, supervised machine learning is a powerful tool for making predictions and classifying data, and it can be easily implemented using programming languages such as Python, R, and Stata. By understanding the fundamentals of supervised learning and applying them in practice, you can leverage the power of machine learning to solve real-world problems.
#Fundamentals #Supervised #Machine #Learning #Applications #Python #Stata #Statistics #Computing
Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks (Information Science and Statistics)
Price:$129.99– $60.22
(as of Dec 24,2024 09:17:38 UTC – Details)
Publisher : Springer; 1999th edition (July 16, 2007)
Language : English
Paperback : 336 pages
ISBN-10 : 0387718230
ISBN-13 : 978-0387718231
Item Weight : 2.31 pounds
Dimensions : 6.1 x 0.76 x 9.25 inches
In this post, we will discuss the book “Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks (Information Science and Statistics)” by Daphne Koller and Nir Friedman.This comprehensive book provides an in-depth look at exact computational methods for Bayesian networks, which are powerful tools for modeling and analyzing complex probabilistic relationships. The authors, both experts in the field, cover a wide range of topics including inference algorithms, learning techniques, and applications of Bayesian networks.
The book is aimed at researchers, students, and practitioners in fields such as artificial intelligence, machine learning, and statistics. It provides a thorough introduction to the principles and techniques of Bayesian networks, as well as practical guidance on how to implement and use these methods in real-world applications.
Overall, “Probabilistic Networks and Expert Systems” is a valuable resource for anyone interested in understanding and utilizing Bayesian networks for probabilistic reasoning and decision making. It is a must-read for those looking to deepen their knowledge and skills in this important area of information science and statistics.
#Probabilistic #Networks #Expert #Systems #Exact #Computational #Methods #Bayesian #Networks #Information #Science #Statistics
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
Price:$89.99– $62.65
(as of Dec 24,2024 07:58:27 UTC – Details)
Publisher : Springer; 2nd edition (February 1, 2009)
Language : English
Hardcover : 745 pages
ISBN-10 : 0387848576
ISBN-13 : 978-0387848570
Item Weight : 2.96 pounds
Dimensions : 9.3 x 6 x 1.4 inchesCustomers say
Customers find the book’s content informative and useful for graduate students interested in building a deeper theoretical understanding of statistical learning. They appreciate the color illustrations and graphics, which are helpful and nicely colored. The coverage is impressive, covering many areas and applications. Many consider the book a good value for money. However, opinions differ on comprehension and chapter organization. Some find the chapters clear and self-contained, with to-the-point concise explanations, while others mention that the chapters are blurry and difficult to understand.
AI-generated from the text of customer reviews
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)Are you interested in diving deeper into the world of statistical learning and data mining? Look no further than “The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
This comprehensive textbook covers the fundamental principles of statistical learning, including topics such as supervised and unsupervised learning, model assessment and selection, and more. The second edition includes new chapters on deep learning, nonparametric regression, and ensemble methods, making it an essential resource for both beginners and experienced practitioners.
Whether you’re a student, researcher, or industry professional, this book offers a thorough introduction to the field of statistical learning and provides valuable insights into how to effectively analyze and interpret data. Pick up your copy today and start mastering the art of data mining, inference, and prediction.
#Elements #Statistical #Learning #Data #Mining #Inference #Prediction #Edition #Springer #Series #Statistics
Probability and Statistics for Machine Learning: A Textbook
Price:$69.99– $65.58
(as of Dec 24,2024 07:36:52 UTC – Details)
Publisher : Springer; 2024th edition (May 15, 2024)
Language : English
Hardcover : 540 pages
ISBN-10 : 3031532813
ISBN-13 : 978-3031532818
Item Weight : 2.5 pounds
Dimensions : 7 x 1.25 x 10 inches
Probability and Statistics for Machine Learning: A TextbookAre you interested in learning more about how probability and statistics play a crucial role in the field of machine learning? Look no further than our comprehensive textbook on Probability and Statistics for Machine Learning.
This textbook covers essential topics such as probability theory, statistical inference, and machine learning algorithms. It provides a solid foundation for understanding the principles behind machine learning models and how they make predictions based on data.
Whether you are a beginner looking to dive into the world of machine learning or an experienced practitioner seeking to deepen your understanding of the underlying mathematics, this textbook is the perfect resource for you.
With clear explanations, real-world examples, and practical exercises, Probability and Statistics for Machine Learning will help you develop the skills and knowledge needed to succeed in this rapidly growing field.
Don’t miss out on this invaluable resource – order your copy today and start mastering the fundamentals of probability and statistics in machine learning.
#Probability #Statistics #Machine #Learning #Textbook
