Titlebook: Machine Learning with PySpark; With Natural Languag Pramod Singh Book 20191st edition Pramod Singh 2019 Machine Learning.PySpark.Python.Sup

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書目名稱Machine Learning with PySpark影響因子(影響力)




書目名稱Machine Learning with PySpark影響因子(影響力)學(xué)科排名




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書目名稱Machine Learning with PySpark網(wǎng)絡(luò)公開度學(xué)科排名




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書目名稱Machine Learning with PySpark被引頻次學(xué)科排名




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書目名稱Machine Learning with PySpark讀者反饋




書目名稱Machine Learning with PySpark讀者反饋學(xué)科排名

貴族

Linear Regression,PySpark and dives deep into the workings of an LR model. It will cover various assumptions to be considered before using LR along with different evaluation metrics. But before even jumping into trying to understand Linear Regression, we must understand the types of variables.

BLA

Random Forests,is also used for Classification/Regression. but in terms of accuracy, random forests beat DT classifiers due to various reasons that we will cover later in the chapter. Let’s learn more about decision trees.

myelography

Recommender Systems,ation is that users have too many options and choices available, yet they don’t like to invest a lot of time going through the entire catalogue of items. Hence, the role of Recommender Systems (RS) becomes critical for recommending relevant items and driving customer conversion.

BET

Conspiracy

Introduction to Machine Learning,earn to recognize a house. We can easily differentiate between a car and a bike just by seeing a few cars and bikes around. We can easily differentiate between a cat and a dog. Even though it seems very easy and intuitive to us as human beings, for machines it can be a herculean task.

會議

Natural Language Processing,slation, recommender systems, spam detection, and sentiment analysis. This chapter demonstrates a series of steps in order to process text data and apply a Machine Learning Algorithm on it. It also showcases the sequence embeddings that can be used as an alternative to traditional input features for classification.

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