Intro to Machine Learning 3 hours to complete Learn the core ideas in machine learning, and build your first models. Pandas 4 hours to complete Solve short hands-on challenges to perfect your data manipulation skills. Build your ML skills in a supportive and helpful community ...
https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12 Vishal Maini的“最佳机器学习资源”本文是上述系列的一部分。我单独提到它是因为它有一套与机器学习相关的非常好非常全面的链接。 https://medium.com/machine-learning-for-humans/how-to-learn-machine-learning-24d53bb...
我们通过如下命令加载并浏览数据: # save filepath to variable for easier accessmelbourne_file_path='../input/melbourne-housing-snapshot/melb_data.csv'#read the data and store data in DataFrame titled melbourne_datamelbourne_data=pd.read_csv(melbourne_file_path)#print a summary of the data in ...
https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12 Vishal Maini的“最佳机器学习资源”本文是上述系列的一部分。我单独提到它是因为它有一套与机器学习相关的非常好非常全面的链接。 https://medium.com/machine-learning-for-humans/how-to-learn-machine-learning-24d53bb...
参考: https://www.kaggle.com/learn/machine-learning-explainability 这个课程将讲解如何从复杂的机器学习模型中解释这些发现。 模型认为数据中的哪些特征是最重要的? 对于来自模型的任何单个预测,数据中的每个特性如何影响该特定预测 每个特性如何影响模型的整体预测(当考虑大量可能的预测时,它的典型影响是什么?) ...
#You imported DecisionTreeRegressor in your last exercise#and that code has been copied to the setup code above. So, no need to#import it again#Specify the modeliowa_model = DecisionTreeRegressor(random_state=1)#Fit iowa_model with the training data.iowa_model.fit(train_X, train_y)#Check...
Kaggle 的比赛任务大致都是根据已知信息，对测试集里的数据进行某种预测。按照主流方法是否涉及到 deep learning 来分，大概有两种比赛：A. 一种比赛比较适合传统的 machine learning 方法，要想取得好成绩，可能要在 feature engineering 和 ensemble 方面大做文章。但基本用不上 deep learning，因此有没有好的 GPU ...
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Kaggle 的比赛任务大致都是根据已知信息,对测试集里的数据进行某种预测。按照主流方法是否涉及到 deep learning 来分,大概有两种比赛: A. 一种比赛比较适合传统的 machine learning 方法,要想取得好成绩,可能要在 feature engineering 和 ensemble 方面大做文章。但基本用不上 deep learning,因此有没有好的 GPU 无...
(k, X_train, y_train, num_epochs, learning_rate, weight_decay, batch_size): train_l_sum, valid_l_sum = 0, 0 for i in range(k): data = get_k_fold_data(k, i, X_train, y_train) net = get_net() train_ls, valid_ls = train(net, *data, num_epochs, learning_rate, ...