AI News, Level-Up Your Machine Learning

Level-Up Your Machine Learning

Since launching Metacademy, I've had a number of people ask , What should I do if I want to get 'better' at machine learning, but I don't know what I want to learn?

then watch as they grimace in the same way an out-of-shape person grimaces when a healthy friend responds with, 'Oh, I watch what I eat and consistently exercise.'

you'll scribble in the margins and dog-ear commonly referenced areas and look for applications of the topics you learn -- the textbook itself becomes a part of your knowledge (the above image shows my nearest textbook).

Key Chapters: It's a short read, and every chapter is fairly illuminating -- though, you can skip the worksheet examples, and chapters 8 and 10 if you're interested in a basic overview.

This will test your ability to manipulate data for a desired machine learning task, and also your ability to apply the correct machine learning technique to a somewhat vague problem.

Expectations: You'll be able to recognize when fundamental machine learning algorithms apply to certain problems and implement functioning machine learning code in R Necessary Background: No real prerequisites, though the following will help (these can be learned/reviewed as you go): Key Chapters: It's a short book, and I recommend all of the chapters -- be sure to actually think through the examples (and type them into R).

This will test your data munging capabilities, your strategy for analyzing a larger dataset, your knowledge of machine learning techniques, and your ability to write analysis code in R.

This will test your ability to understand and interpret cutting-edge machine learning algorithms, approximate and online inference techniques, as well as your implementation chops, your data munging abilities, and your ability to define an interesting application from a vaguely defined problem.

If you're unfamiliar with Bayesian statistics, I recommend studying the first 5 chapters of Doing Bayesian Data Analysis There's a number of subjects you may want to study in depth at the master level: convex optimization, [measure-theoretic] probability theory, discrete optimization, linear algebra, differential geometry, or maybe computational neurology.

PGMs pervade machine learning, and with a strong understanding of this content, you'll be able to dive into most machine learning specialties without too much pain.

Level-Up Your Machine Learning

Since launching Metacademy, I've had a number of people ask , What should I do if I want to get 'better' at machine learning, but I don't know what I want to learn?

then watch as they grimace in the same way an out-of-shape person grimaces when a healthy friend responds with, 'Oh, I watch what I eat and consistently exercise.'

you'll scribble in the margins and dog-ear commonly referenced areas and look for applications of the topics you learn -- the textbook itself becomes a part of your knowledge (the above image shows my nearest textbook).

Key Chapters: It's a short read, and every chapter is fairly illuminating -- though, you can skip the worksheet examples, and chapters 8 and 10 if you're interested in a basic overview.

This will test your ability to manipulate data for a desired machine learning task, and also your ability to apply the correct machine learning technique to a somewhat vague problem.

Expectations: You'll be able to recognize when fundamental machine learning algorithms apply to certain problems and implement functioning machine learning code in R Necessary Background: No real prerequisites, though the following will help (these can be learned/reviewed as you go): Key Chapters: It's a short book, and I recommend all of the chapters -- be sure to actually think through the examples (and type them into R).

This will test your data munging capabilities, your strategy for analyzing a larger dataset, your knowledge of machine learning techniques, and your ability to write analysis code in R.

This will test your ability to understand and interpret cutting-edge machine learning algorithms, approximate and online inference techniques, as well as your implementation chops, your data munging abilities, and your ability to define an interesting application from a vaguely defined problem.

If you're unfamiliar with Bayesian statistics, I recommend studying the first 5 chapters of Doing Bayesian Data Analysis There's a number of subjects you may want to study in depth at the master level: convex optimization, [measure-theoretic] probability theory, discrete optimization, linear algebra, differential geometry, or maybe computational neurology.

PGMs pervade machine learning, and with a strong understanding of this content, you'll be able to dive into most machine learning specialties without too much pain.

Top 25 Best Machine Learning Books You Should Read

There are loads of free resources available online (such as Solutions Review’s buyer’s guides and best practices), and those are great, but sometimes it’s best to do things the old fashioned way.

We’ve carefully selected the top machine learning books based on relevance, popularity, review ratings, publish date, and ability to add business value.

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems “By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems.

Deep Learning (Adaptive Computation and Machine Learning series) “The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology;

Understanding just how these programs and processes function can help you to navigate this new technology. If you’re not familiar with the possibilities of machine learning, you’ll be surprised to see the variety of ways it can be utilized beyond the much-publicized aspects like speech recognition.

The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples.

Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way.”

Many examples are given, with a liberal use of color graphics. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression &

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition “Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library.

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press) “This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) “An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.

Machine Learning: The Art and Science of Algorithms that Make Sense of Data “Peter Flach’s clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss.

Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts.

Deep Learning: A Practicioner’s Approach “This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows.

Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems “With this book you’ll discover all the analytical tools you need to gain insights from complex data and learn how to to choose the correct algorithm for your specific needs.

Through full engagement with the sort of real-world problems data-wranglers face, you’ll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering.

Python Machine Learning, 1st Edition “If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.

Learning From Data Hardcover – 2012

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