###### For question 4, does `predict_probability` output the same probabilities as `predict_proba`? Note that the values have to match one of the class probabilities, not both. To do so, compare the output with: `clf.predict_proba(X)[:,1]`. The shape of the arrays is not important.
###### Does `predict_class` output the same classes as `cfl.predict(X)` for question 5? The shape of the arrays is not important.
###### Does `predict_class` output the same classes as `cfl.predict(X)` for question 5? The shape of the arrays is not important.
###### Does the plot for question 6 look like the plot below? As mentioned, it is not required to shift the class prediction to make the plot easier to understand.
###### Does the plot for question 6 look like the plot below? As mentioned, it is not required to shift the class prediction to make the plot easier to understand.
![alt text][ex3q6]
@ -193,6 +192,7 @@ As said, for some reasons, the results may be slightly different from mine becau
---
#### Bonus
#### Exercise 6: Multi-class (Optional)
##### The exercise is validated if all questions of the exercise are validated
This exercise set focuses on advanced applications of Keras for building and training neural networks. You'll work on both regression and multi-class classification problems, using real-world datasets like the Auto MPG and Iris datasets.
### Role Play
You're a data scientist at a biotech company developing AI-powered systems for various applications. Your current project involves creating neural networks for both regression and multi-class classification tasks. You'll be working on predicting car fuel efficiency and classifying flower species, showcasing the versatility of neural networks in different domains.
This exercise focuses on using Keras to build and train neural networks. Keras is a high-level deep learning API that runs on top of TensorFlow, designed for fast experimentation with deep neural networks. You'll learn to create sequential models, add dense layers, design network architectures, and optimize your models.
This exercise focuses on using Keras to build and train neural networks. Keras is a high-level deep learning API that runs on top of TensorFlow, designed for fast experimentation with deep neural networks. You'll learn to create sequential models, add dense layers, design network architectures, and optimize your models.
@ -126,7 +126,7 @@ X, y, coef = make_regression(n_samples=100,
![alt text][q1]
[q1]: ./w2_day1_ex2_q1.png 'Scatter plot'
[q1]: ./w2_day1_ex2_q1.png "Scatter plot"
2. Fit a LinearRegression from Scikit-learn on the generated data and give the equation of the fitted line. The expected output is: `y = coef * x + intercept`
@ -134,7 +134,7 @@ X, y, coef = make_regression(n_samples=100,
This exercise set focuses on advanced model selection techniques in machine learning. You'll work with cross-validation, grid search, and performance evaluation tools.
This exercise set focuses on advanced model selection techniques in machine learning. You'll work with cross-validation, grid search, and performance evaluation tools.
### Role Play
@ -245,7 +245,7 @@ The plot should look like this:
The interpretation is that from max_depth=10, the train score keeps increasing but the test score (or validation score) reaches a plateau. It means that choosing max_depth = 20 may lead to have an over fitted model.
@ -261,7 +261,7 @@ The interpretation is that from max_depth=10, the train score keeps increasing b
@ -147,7 +147,7 @@ Notice that the neuron **o1** in the output layer takes as input the output of t
In exercise 1, you implemented this neuron.
![alt text][neuron]
[neuron]: ./w3_day1_neuron.png 'Plot'
[neuron]: ./w3_day1_neuron.png "Plot"
Now, we add two more neurons:
@ -156,7 +156,7 @@ Now, we add two more neurons:
![alt text][nn]
[nn]: ./w3_day1_neural_network.png 'Plot'
[nn]: ./w3_day1_neural_network.png "Plot"
1. Implement the function `feedforward` of the class `OurNeuralNetwork` that takes as input the input data and returns the output y. Return the output for these neurons:
“NLP makes it possible for humans to talk to machines:” This branch of AI enables computers to understand, interpret, and manipulate human language. This technology is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data.
### Role Play
You're a Natural Language Processing (NLP) specialist at a tech startup developing a sentiment analysis tool for social media posts. Your task is to build the preprocessing pipeline and create a bag-of-words representation for tweet analysis.
###### For question 1, is validated if the ouptut doesn't contain punctuation ``!"#$%&'()*+,-./:;<=>?@[]^_`{|}~``. Is the previous statement true? Do not take into account the spaces in the output. The output should be as:
###### For question 1, is validated if the ouptut doesn't contain punctuation ``!"#$%&'()*+,-./:;<=>?@[]^_`{|}~``. Is the previous statement true? Do not take into account the spaces in the output. The output should be as:
@ -6,7 +6,7 @@ This set of exercises focuses on using Pandas, a powerful library for data manip
### Role Play
You are a data analyst at a multinational energy company. Your team has been tasked with analyzing various datasets to improve operational efficiency and customer service.
You are a data analyst at a multinational energy company. Your team has been tasked with analyzing various datasets to improve operational efficiency and customer service.
Your manager emphasizes the importance of clean, efficient code and clear explanations of your methods and findings. You'll need to present your results to both technical team members and non-technical executives, so focus on creating clear visualizations and concise summaries of your insights.
Oumaima Fisaoui
2 months ago