practical-machine-learning-with-python
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dipanjanS
Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.
The code in Chapter 9 example on "Analyzing Wine Types" ```wtp_dnn_predictions = le.inverse_transform(wtp_dnn_ypred) ``` throws a warning, and then a fatal error. I don't know how to fix this yet....
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Input In [41], in () 1 # AFTER changing the code, the line runs without error: ----> 3 wtp_dnn_ypred = wtp_dnn_model.predict_class(wtp_test_SX) 4 wtp_dnn_ypred...
`"he'll've": "he he will have"` in the contractions file should be `"he'll've": "he will have"`
Hi, I am trying to use your model evaluation package in google colab and I am getting the error as below: !pip install model_evaluation_utils Collecting model_evaluation_utils ERROR: Could not find...
In [this code](https://github.com/dipanjanS/practical-machine-learning-with-python/blob/master/bonus%20content/feature%20engineering%20text%20data/Feature%20Engineering%20Text%20Data%20-%20Advanced%20Deep%20Learning%20Strategies.ipynb), in the "Get word embeddings" section of the "Skip-gram model", the code is as follows: `merge_layer = model.layers[0]` `word_model = merge_layer.layers[0]` `word_embed_layer = word_model.layers[0]` `weights = word_embed_layer.get_weights()[0][1:]`...
Hello, I hope you're well. I like very much your handbook and real-world examples. My issue is the following concerning the **Chapter 11 - notebook_gold_forecast_arima.ipynb** : in the paragraph 24th...
in the below code with keras latest version, its not working. ``` from keras.layers import Merge from keras.layers.core import Dense, Reshape from keras.layers.embeddings import Embedding from keras.models import Sequential #...
2 print("num of required transactions = ", int(input_assoc_rules.shape[0]*support)) 3 num_trans = input_assoc_rules.shape[0]*support ----> 4 itemsets = dict(frequent_itemsets(data_tran_uk_en, support)) NameError: name 'frequent_itemsets' is not defined