XGBoost or eXtreme Gradient Boosting is a popular scalable machine learning package for tree boosting. Data scientists use it extensively to solve classification, regression, user-defined prediction problems etc. The speed, high-performance, ability to solve real-world scale problems using a minimal amount of resources etc., make XGBoost highly popular among machine learning researchers.
Read more: https://analyticsindiamag.com/8-best-free-resources-to-learn-xgboost/
Swift is a fast and efficient general-purpose programming language that provides real-time feedback and can be seamlessly incorporated into existing Objective-C code. This is why developers are able to write safer, more reliable code while saving time. It aims to be the best language that can be used for various purposes ranging from systems programming to mobile as well as desktop apps and scaling up to cloud services.
Below here, we list down the 10 best online resources to learn Swift language.
(The list is in no particular order)
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Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.
The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.
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The Garrison (armies of militia) of libraries worldwide offer millions of books, such as the Library of Congress in D.C. has over 162 million books, and the New York Public library carries around 53 million books. So many books, so little time in a human’s life.
A number of people have asked me through several of my channels and conferences — how to find time to read books, and what can be done to read more books each month. Some audiences even feel that 43 machine learning books in a year are insufficient, and want more.
I keep discovering new material every day on top of the antiquated books, which still offer good concepts. To get started, I would suggest disconnecting from Netflix, Amazon Video, and regular TV channels. The more you watch any of this stuff, the more you wouldn’t be finding time to read the books.
In 2020, I had managed to read more than 96,120 pieces of books, eBooks, articles, averaging 267 pieces of books, eBooks, research papers, or articles per day. However, on average, people might have read 10 to 30 machine learning books in a year.
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Being an award-winning machine learning company in India, we provide advanced machine learning solutions at 60% less cost. We have India’s best machine learning and artificial intelligence development team that helps businesses think, predict & act smartly in this digital era.
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Scikit-Learn is one of the popular software machine learning libraries. The library is built on top of NumPy, SciPy, and Matplotlib and supports supervised and unsupervised learning as well as provides various tools for model fitting, data preprocessing, model selection and evaluation.
About: From the developers of Scikit-Learn, this tutorial provides an introduction to machine learning with Scikit-Learn. It includes topics such as problem setting, loading an example dataset, learning and predicting. The tutorial is suitable for both beginners and advanced students.
**About: **In this project-based course, you will learn the fundamentals of sentiment analysis, and build a logistic regression model to classify movie reviews as either positive or negative. You will learn how to develop and employ a logistic regression classifier using Scikit-Learn, perform feature extraction with The Natural Language Toolkit (NLTK), tune model hyperparameters and evaluate model accuracy etc.
**About: **Python Machine Learning: Scikit-Learn tutorial will help you learn the basics of Python machine learning. You will learn how to use Python and its libraries to explore your data with the help of Matplotlib and Principal Component Analysis (PCA). You will also learn how to work with the KMeans algorithm to construct an unsupervised model, fit this model to your data, predict values, and validate the model.
**About: **Edureka’s video tutorial introduces machine learning in Python. It will take you through regression and clustering techniques along with a demo of SVM classification on the famous iris dataset. This video helps you to learn the introduction to Scikit-learn and how to install it, understand how machine learning works, among other things.
About: In this Coursera offering, you will learn about Linear Regression, Regression using Random Forest Algorithm, Regression using Support Vector Machine Algorithm. Scikit-Learn provides a comprehensive array of tools for building regression models.
About: In this course, you will learn about machine learning, algorithms, and how Scikit-Learn makes it all so easy. You will get to know the machine learning approach, jargons to understand a dataset, features of supervised and unsupervised learning models, algorithms such as regression, classification, clustering, and dimensionality reduction.
About: In this two-hour long project-based course, you will build and evaluate a simple linear regression model using Python. You will employ the Scikit-Learn module for calculating the linear regression while using pandas for data management and seaborn for plotting. By the end of this course, you will be able to build a simple linear regression model in Python with Scikit-Learn, employ Exploratory Data Analysis (EDA) to small data sets with seaborn and pandas.
**About: **This tutorial is available on GitHub. It includes an introduction to machine learning with sample applications, data formats, preparation and representation, supervised learning: training and test data, the Scikit-Learn estimator interface and more.
About: This is a two-hour long project-based course, where you will understand the business problem and the dataset and learn how to generate a hypothesis to create new features based on existing data. You will learn to perform text pre-processing and create custom transformers to generate new features. You will also learn to implement an NLP pipeline, create custom transformers and build a text classification model.
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