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Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes

Home Page: https://www.coursera.org/specializations/mathematics-for-machine-learning-and-data-science

Jupyter Notebook 91.98% Python 8.02%
coursera-data-science coursera-machine-learning coursera-mathematics coursera-specialization deeplearning-ai probability python statistics coursera-assignment machine-learning

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Mathematics for Machine Learning and Data Science Specialization - Coursera

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Mathematics for Machine Learning and Data Science Specialization offered by deeplearning.ai , instructed by Luis Serrano on Coursera.























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mathematics-for-machine-learning-and-data-science-specialization-coursera's Issues

Help! C3_W1_Assignment Exercise 6 and Exercise 7 shows error!

I couldn't complete the assignment and started looking online for answers and found your notebook.

Exercise 6 shows the following error :
AttributeError Traceback (most recent call last)
Cell In[103], line 7
4 example_breed = df_test[["breed"]].loc[0]["breed"]
5 print(f"Example dog has breed {example_breed} and features: height = {example_dog['height']:.2f}, weight = {example_dog['weight']:.2f}, bark_days = {example_dog['bark_days']:.2f}, ear_head_ratio = {example_dog['ear_head_ratio']:.2f}\n")
----> 7 print(f"Probability of these features if dog is classified as breed 0: {prob_of_X_given_C([*example_dog], FEATURES, 0, train_params)}")
8 print(f"Probability of these features if dog is classified as breed 1: {prob_of_X_given_C([*example_dog], FEATURES, 1, train_params)}")
9 print(f"Probability of these features if dog is classified as breed 2: {prob_of_X_given_C([*example_dog], FEATURES, 2, train_params)}")

Cell In[102], line 33, in prob_of_X_given_C(X, features, breed, params_dict)
29 match feature:
30 # You can add add as many case statements as you see fit
31 case "height" | "weight":
32 # Compute the relevant pdf given the distribution and the estimated parameters
---> 33 probability_f = pdf_gaussian(x, params.mu, params.sigma)
35 case "bark_days":
36 # Compute the relevant pdf given the distribution and the estimated parameters
37 probability_f = pdf_binomial(x, params.n, params.p)

AttributeError: 'dict' object has no attribute 'mu'

Exercise 7 shows the following error:
TypeError Traceback (most recent call last)
Cell In[99], line 3
1 # Test your function
----> 3 example_pred = predict_breed([*example_dog], FEATURES, train_params, train_class_probs, example_breed)
4 print(f"Example dog has breed {example_breed} and Naive Bayes classified it as {example_pred}")

TypeError: predict_breed() takes 4 positional arguments but 5 were given

Thank you for any help.

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