Ebook Þ Hands-On Machine Learning with Scikit-Learn and

Ebook Þ Hands-On Machine Learning with Scikit-Learn and


Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems ☆ [PDF / Epub] ★ Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems By Aurélien Géron ✩ – Centrumpowypadkowe.co.uk Graphics in this book are printed in black and whiteThrough a series of recent breakthroughs, deep learning has boosted the entire field of machine learning Now, even programmers who know close to not Learning with PDF/EPUB ¾ Graphics in this book are printed in black and whiteThrough a series Hands-On Machine PDF \ of recent breakthroughs, deep learning has boosted the entire field of machine learning Machine Learning with Kindle Ï Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data This practical book shows you howBy using concrete examples, minimal theory, and two production ready Python frameworks scikit learn and TensorFlow author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems You ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks With exercises in each chapter to help you apply what you ve learned, all you need is programming experience to get startedExplore the machine learning landscape, particularly neural netsUse scikit learn to track an example machine learning project end to endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details.

    Kindle Welcome to the Kindle ereader store technology can use simple, efficient tools to implement programs capable of learning from data This practical book shows you howBy using concrete examples, minimal theory, and two production ready Python frameworks scikit learn and TensorFlow author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems You ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks With exercises in each chapter to help you apply what you ve learned, all you need is programming experience to get startedExplore the machine learning landscape, particularly neural netsUse scikit learn to track an example machine learning project end to endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details."/>
  • ebook
  • 574 pages
  • Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems
  • Aurélien Géron
  • 08 December 2019
  • 1491962259

10 thoughts on “Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems

  1. ☘Misericordia☘ ~ The Serendipity Aegis ~ ⚡ϟ⚡ϟ⚡⛈ ✺❂❤❣ ☘Misericordia☘ ~ The Serendipity Aegis ~ ⚡ϟ⚡ϟ⚡⛈ ✺❂❤❣ says:

    A really nice and sensible intro to some of the most salient ML topics Really visual and nifty in explanations, scikit TF oriented Q When most people hear Machine Learning, they picture a robot a dependable butler or a deadlyTerminator depending on who you ask But Machine Learning is not just a futuristic fantasy, it s alreadyhere In fact, it has been around for decades in some specialized applications, such asOptical Character Recognition OCR But the first ML application that really b A really nice and sensible intro to some of the most salient ML topics Really visual and nifty in explanations, scikit TF oriented Q When most people hear Machine Learning, they picture a robot a dependable butler or a deadlyTerminator depending on who you ask But Machine Learning is not just a futuristic fantasy, it s alreadyhere In fact, it has been around for decades in some specialized applications, such asOptical Character Recognition OCR But the first ML application that really became mainstream, improving the lives ofhundreds of millions of people, took over the world back in the 1990s it was the spam filter Not exactlya self aware Skynet, but it does technically qualify as Machine Learning it has actually learned so wellthat you seldom need to flag an email as spam any It was followed by hundreds of ML applicationsthat now quietly power hundreds of products and features that you use regularly, from better recommendations to voice search.Where does Machine Learning start and where does it end What exactly does it mean for a machine to learn something If I download a copy of Wikipedia, has my computer really learned something Is itsuddenly smarter In this chapter we will start by clarifying what Machine Learning is and why you maywant to use it c Topics Q Here are some of the most important supervised learning algorithms covered in this book k Nearest NeighborsLinear RegressionLogistic RegressionSupport Vector Machines SVMs Decision Trees and Random ForestsNeural networks c Q Here are some of the most important unsupervised learning algorithms we will cover dimensionality reduction in Chapter 8 Clustering k MeansHierarchical Cluster Analysis HCA Expectation Maximization Visualization and dimensionality reduction Principal Component Analysis PCA Kernel PCALocally Linear Embedding LLE t distributed Stochastic Neighbor Embedding t SNE Association rule learning AprioriEclat c

  2. Mohamed Mohamed says:

    One of the best ML books out there Dives deep into the practical implementation of Sklearn and Tensorflow Also, dives deep enough into the math side of ML Read it from cover to cover Really worth it.

  3. Mihail Burduja Mihail Burduja says:

    The book contains a chapter that shows a basic flow for working with data problems The TF chapters are interesting but somehow short I would have likedon convolutional layers and RNN.The reinforcement learning chapter is very interesting.

  4. Eugene Eugene says:

    great introduction into machine learning for both developer and non developers authors suggests to just go through even if you don t understand math details main points are extraction of field expert knowledge is very important you should know which model will serve better for the given solution luckily lot of models are available already from other scientists training data is the most important part theyou have it the better so if you can you should accumulate as much data as great introduction into machine learning for both developer and non developers authors suggests to just go through even if you don t understand math details main points are extraction of field expert knowledge is very important you should know which model will serve better for the given solution luckily lot of models are available already from other scientists training data is the most important part theyou have it the better so if you can you should accumulate as much data as you can, preferably categorized you may not still know how you will apply the accumulated data in the future but you will need it labeling training data is very important too to train neural network you need to have at least thousands of labeled data samples thethe better Machine learning algorithms and neural networks are pretty common for years but latest breakthrough is possible because of new optimization , new autoencoders that may help to artificially generate training data allowing to do training faster and with less data machine learning is still pretty time and resources consuming process to train machine learning model you need to know how to tweak parameters and how to use different training approaches fitting the particular model.the book demonstrate including the code different approaches using SciLearn python package and also the TensorFlow

  5. Wanasit Wanasit says:

    At the time of reading, I had already learned about most concepts in the book So, I focused only on the deep parts of Tensorflow It s a good book overall I imagine it would be very useful for myself a few years ago.My favorite part is the reinforce learning in the last chapter The chapter makes sense, is easy to understand, and its example is very practical.

  6. Edaena Edaena says:

    This is the best book I ve read on machine learning It is well written and the examples are very good with real data sets.The first half is an introduction to machine learning and the second half explores deep learning It is a great book to read along an online course.

  7. Lara Thompson Lara Thompson says:

    A very excellent introduction to many machine learning algorithms beginning at the very beginning and ending much further than I expected I can t wait for the updated edition to reference because, yes, many tensorflow functions changed name.

  8. Ferhat Culfaz Ferhat Culfaz says:

    5 for the first half of the book, scikit learn 3 for the second half, Tensor Flow Nice examples with Jupyter notebooks Good mix of practical with theoretical The scikit learn section is a great reference, nice detailed explanation with good references for further reading to deepen your knowledge The tensor flow part is weaker as examples becomecomplex Chollet s book Deep Learning with Python, which uses Keras is much stronger, as the examples are easier to understand as Keras is a 5 for the first half of the book, scikit learn 3 for the second half, Tensor Flow Nice examples with Jupyter notebooks Good mix of practical with theoretical The scikit learn section is a great reference, nice detailed explanation with good references for further reading to deepen your knowledge The tensor flow part is weaker as examples becomecomplex Chollet s book Deep Learning with Python, which uses Keras is much stronger, as the examples are easier to understand as Keras is a simple layer over tensor flow to ease the use Also Chollet explains the concepts better and nicely annotates his code.Buy this book for scikit learn and overall best practise for machine learning and data science Buy Chollet s Deep Learning using Python for practical deep learning itself.Overall still a practical book with Jupyter Notebook supplementary material

  9. Omri Har-shemesh Omri Har-shemesh says:

    Great book for introduction to machine learning using Scikit Learn I didn t like as much the part about Tensorflow but the scikit leran one is great.

  10. Fernando Flores Fernando Flores says:

    Nicely well explained from scratch to advanced

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