Link to the GitHub Repository containing the code examples and additionalmaterial: [ of the most innovative breakthroughs and exciting new technologies can beattributed to applications of machine learning. We are living in an age wheredata comes in abundance, and thanks to the self-learning algorithms from thefield of machine learning, we can turn this data into knowledge. Automatedspeech recognition on our smart phones, web search engines, e-mail spamfilters, the recommendation systems of our favorite movie streaming services –machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed inrecent years, machine learning is now right at our fingertips. Python providesthe perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how toutilize these in real-world applications using Python. Step-by-step, you willexpand your skill set with the best practices for transforming raw data intouseful information, developing learning algorithms efficiently, and evaluatingresults.You will discover the different problem categories that machine learning cansolve and explore how to classify objects, predict continuous outcomes withregression analysis, and find hidden structures in data via clustering. Youwill build your own machine learning system for sentiment analysis andfinally, learn how to embed your model into a web app to share with the worldwords : 87138

trantor/en/Beyeler, Michael/Machine Learning for OpenCV.epub

zlib/no-category/Beyeler, Michael/Machine Learning for OpenCV_30615697.epub

Python Machine Learning : Learn How to Build Powerful Python Machine Learning Algorithms to Generate Useful Data Insights with This Data Analysis Tutorial

Machine learning for OpenCV : a practical introduction to the world of machine learning and image processing using OpenCV and Python

Machine learning for OpenCV a practical introduction to the world of machine learning and image processing using Open CV and Python

Python machine learning : unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics

Python и машинное обучение: наука и искусство построения алгоритмов, которые извлекают знания из данных

Machine Learning for OpenCV: Intelligent image processing with Python

Python Machine Learning, 1st Edition

Себастьян Рашка; перевод с англ. А. В. Логунова

Raschka, Sebastian

Sebastian Raschka

Рашка, Себастьян

Michael Beyeler

ДМК Пресс

Community experience distilled, Community experience distilled, England, 2016

Community experience distilled, Birmingham, UK, 2015

United Kingdom and Ireland, United Kingdom

Packt Publishing, Birmingham, UK, 2017

Цветное издание, Москва, Russia, 2017

Birmingham Mumbai, 2017

1st edition, 2015

Jul 14, 2017

2017-07-14

Includes index.

gaaagpl

Предм. указ.: с. 408-417
Пер.: Raschka, Sebastian Python machine learning Birmingham ; Mumbai : Packt, cop. 2016 978-1-78355-513-0

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<p>Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics<br></p><p>About This Book<br></p><ul> <li>Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization </li> <li>Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms </li> <li>Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets </li></ul><p>Who This Book Is For<br></p><p>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.<br></p><p>What You Will Learn<br></p><ul> <li>Explore how to use different machine learning models to ask different questions of your data </li> <li>Learn how to build neural networks using Keras and Theano </li> <li>Find out how to write clean and elegant Python code that will optimize the strength of your algorithms </li> <li>Discover how to embed your machine learning model in a web application for increased accessibility </li> <li>Predict continuous target outcomes using regression analysis </li> <li>Uncover hidden patterns and structures in data with clustering </li> <li>Organize data using effective pre-processing techniques </li> <li>Get to grips with sentiment analysis to delve deeper into textual and social media data </li></ul><p>In Detail<br></p><p>Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.<br></p><p>Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.<br></p><p>Style and approach<br></p><p>Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.<br></p>

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book* Load, store, edit, and visualize data using OpenCV and Python* Grasp the fundamental concepts of classification, regression, and clustering* Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide* Evaluate, compare, and choose the right algorithm for any taskWho This Book Is ForThis book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks. What You Will Learn* Explore and make effective use of OpenCV's machine learning module* Learn deep learning for computer vision with Python* Master linear regression and regularization techniques* Classify objects such as flower species, handwritten digits, and pedestrians* Explore the effective use of support vector machines, boosted decision trees, and random forests* Get acquainted with neural networks and Deep Learning to address real-world problems* Discover hidden structures in your data using k-means clustering* Get to grips with data pre-processing and feature engineeringIn DetailMachine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google's DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!Style and approachOpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk - and answer - tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf 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. What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Pylearn 2 and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsKey FeaturesLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk – and answer – tough questions of your data with robust statistical models, built for a range of datasetsBook DescriptionMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. What you will learnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Keras and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataWho this book is forIf 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.

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask -- and answer -- tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For 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. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Pylearn 2 and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data -- its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer som..

Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book Load, store, edit, and visualize data using OpenCV and Python Grasp the fundamental concepts of classification, regression, and clustering Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide Evaluate, compare, and choose the right algorithm for any task Who This Book Is For This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks. What You Will Learn Explore and make effective use of OpenCV's machine learning module Learn deep learning for computer vision with Python Master linear regression and regularization techniques Classify objects such as flower species, handwritten digits, and pedestrians Explore the effective use of support vector machines, boosted decision trees, and random forests Get acquainted with neural networks and Deep Learning to address real-world problems Discover hidden structures in your data using k-means clustering Get to grips with data pre-processing and feature engineering In Detail Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google's DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take o..

Implementing a bagging regressor -- Understanding boosting ensembles -- Implementing a boosting classifier -- Implementing a boosting regressor -- Understanding stacking ensembles -- Combining decision trees into a random forest -- Understanding the shortcomings of decision trees -- Implementing our first random forest -- Implementing a random forest with scikit-learn -- Implementing extremely randomized trees -- Using random forests for face recognition -- Loading the dataset -- Preprocessing the dataset -- Training and testing the random forest -- Implementing AdaBoost -- Implementing AdaBoost in OpenCV -- Implementing AdaBoost in scikit-learn -- Combining different models into a voting classifier -- Understanding different voting schemes -- Implementing a voting classifier -- Summary -- Chapter 11: Selecting the Right Model with Hyperparameter Tuning -- Evaluating a model -- Evaluating a model the wrong way -- Evaluating a model in the right way -- Selecting the best model -- Understanding cross-validation -- Manually implementing cross-validation in OpenCV -- Using scikit-learn for k-fold cross-validation -- Implementing leave-one-out cross-validation -- Estimating robustness using bootstrapping -- Manually implementing bootstrapping in OpenCV -- Assessing the significance of our results -- Implementing Student's t-test -- Implementing McNemar's test -- Tuning hyperparameters with grid search -- Implementing a simple grid search -- Understanding the value of a validation set -- Combining grid search with cross-validation -- Combining grid search with nested cross-validation -- Scoring models using different evaluation metrics -- Choosing the right classification metric -- Choosing the right regression metric -- Chaining algorithms together to form a pipeline -- Implementing pipelines in scikit-learn -- Using pipelines in grid searches -- Summary

Cover -- Copyright -- Credits -- Foreword -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: A Taste of Machine Learning -- Getting started with machine learning -- Problems that machine learning can solve -- Getting started with Python -- Getting started with OpenCV -- Installation -- Getting the latest code for this book -- Getting to grips with Python's Anaconda distribution -- Installing OpenCV in a conda environment -- Verifying the installation -- Getting a glimpse of OpenCV's ML module -- Summary -- Chapter 2: Working with Data in OpenCV and Python -- Understanding the machine learning workflow -- Dealing with data using OpenCV and Python -- Starting a new IPython or Jupyter session -- Dealing with data using Python's NumPy package -- Importing NumPy -- Understanding NumPy arrays -- Accessing single array elements by indexing -- Creating multidimensional arrays -- Loading external datasets in Python -- Visualizing the data using Matplotlib -- Importing Matplotlib -- Producing a simple plot -- Visualizing data from an external dataset -- Dealing with data using OpenCV's TrainData container in C++ -- Summary -- Chapter 3: First Steps in Supervised Learning -- Understanding supervised learning -- Having a look at supervised learning in OpenCV -- Measuring model performance with scoring functions -- Scoring classifiers using accuracy, precision, and recall -- Scoring regressors using mean squared error, explained variance, and R squared -- Using classification models to predict class labels -- Understanding the k-NN algorithm -- Implementing k-NN in OpenCV -- Generating the training data -- Training the classifier -- Predicting the label of a new data point -- Using regression models to predict continuous outcomes -- Understanding linear regression

Knowing the limitations of expectation-maximization -- First caveat: No guarantee of finding the global optimum -- Second caveat: We must select the number of clusters beforehand -- Third caveat: Cluster boundaries are linear -- Fourth caveat: k-means is slow for a large number of samples -- Compressing color spaces using k-means -- Visualizing the true-color palette -- Reducing the color palette using k-means -- Classifying handwritten digits using k-means -- Loading the dataset -- Running k-means -- Organizing clusters as a hierarchical tree -- Understanding hierarchical clustering -- Implementing agglomerative hierarchical clustering -- Summary -- Chapter 9: Using Deep Learning to Classify Handwritten Digits -- Understanding the McCulloch-Pitts neuron -- Understanding the perceptron -- Implementing your first perceptron -- Generating a toy dataset -- Fitting the perceptron to data -- Evaluating the perceptron classifier -- Applying the perceptron to data that is not linearly separable -- Understanding multilayer perceptrons -- Understanding gradient descent -- Training multi-layer perceptrons with backpropagation -- Implementing a multilayer perceptron in OpenCV -- Preprocessing the data -- Creating an MLP classifier in OpenCV -- Customizing the MLP classifier -- Training and testing the MLP classifier -- Getting acquainted with deep learning -- Getting acquainted with Keras -- Classifying handwritten digits -- Loading the MNIST dataset -- Preprocessing the MNIST dataset -- Training an MLP using OpenCV -- Training a deep neural net using Keras -- Preprocessing the MNIST dataset -- Creating a convolutional neural network -- Fitting the model -- Summary -- Chapter 10: Combining Different Algorithms into an Ensemble -- Understanding ensemble methods -- Understanding averaging ensembles -- Implementing a bagging classifier

Using linear regression to predict Boston housing prices -- Loading the dataset -- Training the model -- Testing the model -- Applying Lasso and ridge regression -- Classifying iris species using logistic regression -- Understanding logistic regression -- Loading the training data -- Making it a binary classification problem -- Inspecting the data -- Splitting the data into training and test sets -- Training the classifier -- Testing the classifier -- Summary -- Chapter 4: Representing Data and Engineering Features -- Understanding feature engineering -- Preprocessing data -- Standardizing features -- Normalizing features -- Scaling features to a range -- Binarizing features -- Handling the missing data -- Understanding dimensionality reduction -- Implementing Principal Component Analysis (PCA) in OpenCV -- Implementing Independent Component Analysis (ICA) -- Implementing Non-negative Matrix Factorization (NMF) -- Representing categorical variables -- Representing text features -- Representing images -- Using color spaces -- Encoding images in RGB space -- Encoding images in HSV and HLS space -- Detecting corners in images -- Using the Scale-Invariant Feature Transform (SIFT) -- Using Speeded Up Robust Features (SURF) -- Summary -- Chapter 5: Using Decision Trees to Make a Medical Diagnosis -- Understanding decision trees -- Building our first decision tree -- Understanding the task by understanding the data -- Preprocessing the data -- Constructing the tree -- Visualizing a trained decision tree -- Investigating the inner workings of a decision tree -- Rating the importance of features -- Understanding the decision rules -- Controlling the complexity of decision trees -- Using decision trees to diagnose breast cancer -- Loading the dataset -- Building the decision tree -- Using decision trees for regression -- Summary

Chapter 6: Detecting Pedestrians with Support Vector Machines -- Understanding linear support vector machines -- Learning optimal decision boundaries -- Implementing our first support vector machine -- Generating the dataset -- Visualizing the dataset -- Preprocessing the dataset -- Building the support vector machine -- Visualizing the decision boundary -- Dealing with nonlinear decision boundaries -- Understanding the kernel trick -- Knowing our kernels -- Implementing nonlinear support vector machines -- Detecting pedestrians in the wild -- Obtaining the dataset -- Taking a glimpse at the histogram of oriented gradients (HOG) -- Generating negatives -- Implementing the support vector machine -- Bootstrapping the model -- Detecting pedestrians in a larger image -- Further improving the model -- Summary -- Chapter 7: Implementing a Spam Filter with Bayesian Learning -- Understanding Bayesian inference -- Taking a short detour on probability theory -- Understanding Bayes' theorem -- Understanding the naive Bayes classifier -- Implementing your first Bayesian classifier -- Creating a toy dataset -- Classifying the data with a normal Bayes classifier -- Classifying the data with a naive Bayes classifier -- Visualizing conditional probabilities -- Classifying emails using the naive Bayes classifier -- Loading the dataset -- Building a data matrix using Pandas -- Preprocessing the data -- Training a normal Bayes classifier -- Training on the full dataset -- Using n-grams to improve the result -- Using tf-idf to improve the result -- Summary -- Chapter 8: Discovering Hidden Structures with Unsupervised Learning -- Understanding unsupervised learning -- Understanding k-means clustering -- Implementing our first k-means example -- Understanding expectation-maximization -- Implementing our own expectation-maximization solution

Link to the GitHub Repository containing the code examples and additional
material: [https://github.com/rasbt/python-machi...](https://github.com/rasbt
/python-machine-learning-book)
Many of the most innovative breakthroughs and exciting new technologies can be
attributed to applications of machine learning. We are living in an age where
data comes in abundance, and thanks to the self-learning algorithms from the
field of machine learning, we can turn this data into knowledge. Automated
speech recognition on our smart phones, web search engines, e-mail spam
filters, the recommendation systems of our favorite movie streaming services –
machine learning makes it all possible.
Thanks to the many powerful open-source libraries that have been developed in
recent years, machine learning is now right at our fingertips. Python provides
the perfect environment to build machine learning systems productively.
This book will teach you the fundamentals of machine learning and how to
utilize these in real-world applications using Python. Step-by-step, you will
expand your skill set with the best practices for transforming raw data into
useful information, developing learning algorithms efficiently, and evaluating
results.
You will discover the different problem categories that machine learning can
solve and explore how to classify objects, predict continuous outcomes with
regression analysis, and find hidden structures in data via clustering. You
will build your own machine learning system for sentiment analysis and
finally, learn how to embed your model into a web app to share with the world
python,Programming

Link to the GitHub Repository containing the code examples and additional material: (https://github.com/rasbt/python-machine-learning-book) https://github.com/rasbt/python-machi...
Many of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services machine learning makes it all possible.
Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.
This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.
You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

Giving computers the ability to learn from data
Training machine learning algorithms for classification
A tour of machine learning classifiers using Scikit-learn
Building good training sets : data preprocessing
Compressing data via dimensionality reduction
Learning best practices for model evaluation and hyperparameter tuning
Combining different models for ensemble learning
Applying machine learning to sentiment analysis
Embedding a machine learning model into a web application
Predicting continuous target variables with regression analysis
Working with unlabeled data : clustering analysis
Training artificial neural networks for image recognition
Parallelizing neural network training with Theano.

2024-06-27