fix: Module 07 corrected

42-AI · Jun 11, 2024 · 82a2ef8 · 82a2ef8
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diff --git a/module05/usefull_ressources.tex b/module05/usefull_ressources.tex
@@ -15,7 +15,7 @@ \section*{Notions of the module}
 \section*{Useful Ressources}
 
 You are strongly advise to use the following resource:
-\href{https://www.coursera.org/learn/machine-learning/home/week/1}{Machine Learning MOOC - Stanford}
+\href{https://www.coursera.org/learn/machine-learning}{Machine Learning MOOC - Stanford}
 These videos are available at no cost; simply log in, select "Enroll for Free", and choose "audit the course for free" in the popup window.
 The following sections of the course are pertinent to today's exercises: 
 

diff --git a/module06/usefull_ressources.tex b/module06/usefull_ressources.tex
@@ -14,7 +14,7 @@ \section*{Notions of the module}
 \section*{Useful Ressources}
 
 You are strongly advise to use the following resource:
-\href{https://www.coursera.org/learn/machine-learning/home/week/1}{Machine Learning MOOC - Stanford}
+\href{https://www.coursera.org/learn/machine-learning}{Machine Learning MOOC - Stanford}
 These videos are available at no cost; simply log in, select "Enroll for Free", and choose "audit the course for free" in the popup window.
 The following sections of the course are pertinent to today's exercises: 
 

diff --git a/module07/en.py_proj.tex b/module07/en.py_proj.tex
@@ -24,8 +24,7 @@ \chapter*{Common Instructions}
 
   \item Your manual is the internet.
 
-  \item You can also ask questions in the \texttt{\#bootcamps} channel in the \href{https://42-ai.slack.com}{42AI}
-  or \href{42born2code.slack.com}{42born2code}.
+  \item You can also ask questions on the {https://discord.gg/8Vvb6QMCZq}{42AI} discord.
 
   \item If you find any issue or mistakes in the subject please create an issue on \href{https://github.com/42-AI/bootcamp_python/issues}{42AI repository on Github}.  
 

diff --git a/module07/en.subject.tex b/module07/en.subject.tex
@@ -234,7 +234,7 @@ \section*{Examples}
 
 # Example 3:
 theta3 = np.array([-1.5, 0.6, 2.3, 1.98]).reshape((-1, 1))
-simple_predict(X, theta3)
+simple_predict(x, theta3)
 # Output:
 array([[ 9.64], [24.28], [38.92], [53.56]])
 
@@ -357,7 +357,7 @@ \section*{Examples}
 
 # Example 3:
 theta3 = np.array([-1.5, 0.6, 2.3, 1.98]).reshape((-1, 1))
-predict_(X, theta3)
+predict_(x, theta3)
 # Output:
 array([[ 9.64], [24.28], [38.92], [53.56]])
 
@@ -524,7 +524,7 @@ \section*{Examples}
         [  1,  -5,  11],
         [  9, -11,   8]])
 y = np.array([2, 14, -13, 5, 12, 4, -19]).reshape((-1, 1))
-theta1 = np.array([3,0.5,-6]).reshape((-1, 1))
+theta1 = np.array([0, 3, 0.5, -6]).reshape((-1, 1))
 
 # Example :
 gradient(x, y, theta1)
@@ -533,7 +533,7 @@ \section*{Examples}
 
 
 # Example :
-theta2 = np.array([0,0,0]).reshape((-1, 1))
+theta2 = np.array([0, 0, 0, 0]).reshape((-1, 1))
 gradient(x, y, theta2)
 # Output:
 array([[ -0.71428571], [  0.85714286], [23.28571429], [-26.42857143]])
@@ -549,6 +549,7 @@ \section*{Examples}
 \chapter{Exercise 04}
 \extitle{Multivariate Gradient Descent}
 \input{en.ex04_interlude.tex}
+\newpage
 \turnindir{ex04}
 \exnumber{04}
 \exfiles{fit.py}
@@ -708,7 +709,7 @@ \section*{Examples}
 mylr.alpha = 1.6e-4
 mylr.max_iter = 200000
 mylr.fit_(X, Y)
-mylr.theta
+mylr.thetas
 # Output:
 array([[18.188..], [2.767..], [-0.374..], [1.392..], [0.017..]])
 
@@ -856,13 +857,13 @@ \subsubsection*{Examples}
 data = pd.read_csv("spacecraft_data.csv")
 X = np.array(data[['Age']])
 Y = np.array(data[['Sell_price']])
-myLR_age = MyLR(theta = [[1000.0], [-1.0]], alpha = 2.5e-5, max_iter = 100000)
-myLR_age.fit_(X[:,0].reshape(-1,1), Y)
+myLR_age = MyLR(thetas = [[1000.0], [-1.0]], alpha = 2.5e-5, max_iter = 100000)
+myLR_age.fit_(X, Y)
 
-y_pred = myLR_age.predict_(X[:,0].reshape(-1,1)) 
-myLR_age.mse_(y_pred,Y)
+y_pred = myLR_age.predict_(X)
+myLR_age.mse_(Y, y_pred)
 #Output
-55736.86719...
+55736.867198...
 \end{minted}
 
 How accurate is your model when you only take one feature into account?
@@ -905,26 +906,29 @@ \subsubsection*{Examples}
 data = pd.read_csv("spacecraft_data.csv")
 X = np.array(data[['Age','Thrust_power','Terameters']])
 Y = np.array(data[['Sell_price']])
-my_lreg = MyLR(theta = [1.0, 1.0, 1.0, 1.0], , alpha = 1e-4, max_iter = 600000)
+my_lreg = MyLR(thetas=[1.0, 1.0, 1.0, 1.0], alpha=9e-5, max_iter=500000)
 
 
 # Example 0:
-my_lreg.mse_(X,Y)
+my_lreg.mse_(Y, my_lreg.predict_(X))
 # Output:
 144044.877...
 
 
 # Example 1:
-my_lreg.fit_(X,Y)
-my_lreg.theta
+my_lreg.fit_(X, Y)
+my_lreg.thetas
 # Output:
-array([[334.994...],[-22.535...],[5.857...],[-2.586...]])
+array([[367.28849...]
+       [-23.69939...]
+       [  5.73622...]
+       [ -2.63855...]])
 
 
 # Example 2:
-my_lreg.mse_(X,Y)
+print(my_lreg.mse_(Y, my_lreg.predict_(X)))
 # Output:
-586.896999...
+435.9325695...
 \end{minted}
 
 % ================================= %

diff --git a/module07/usefull_ressources.tex b/module07/usefull_ressources.tex
@@ -15,17 +15,27 @@ \section*{Notions of the module}
 \section*{Useful Ressources}
 
 You are strongly advise to use the following resource:
-\href{https://www.coursera.org/learn/machine-learning/home/week/2}{Machine Learning MOOC - Stanford}
-Here are the sections of the MOOC that are relevant for today's exercises: 
+\href{https://www.coursera.org/learn/machine-learning}{Machine Learning MOOC - Stanford}
+These videos are available at no cost; simply log in, select "Enroll for Free", and choose "audit the course for free" in the popup window.
+The following sections of the course are pertinent to today's exercises:
 
-\subsection*{Week 2}
+\subsection*{Week 2: Regression with multiple input variables}
 
-\subsubsection*{Multivariate Linear Regression}
+\subsubsection*{Multiple linear regression}
 \begin{itemize}
-  \item Multiple Features (Video + Reading)
-  \item Gradient Descent for Multiple Variables (Video + Reading)
-  \item Gradient Descent in Practice I- Feature Scaling (Video + Reading)
-  \item Gradient Descent in Practice II- Learning Rate (Video + Reading)
-  \item Features and Polynomial Regression (Video + Reading)
-  \item Review (Reading + Quiz)
+  \item Multiple features
+  \item Gradient descent for multiple linear regression
 \end{itemize}
+
+\subsubsection*{Gradient descent in practice}
+\begin{itemize}
+  \item Feature scaling part 1
+  \item Feature scaling part 2
+  \item Checking gradient descent for convergence
+  \item Choosing the learning rate
+  \item Feature engineering
+  \item Polynomial regression
+\end{itemize}
+
+
+\emph{All videos above are available also on this \href{https://youtube.com/playlist?list=PLkDaE6sCZn6FNC6YRfRQc_FbeQrF8BwGI&feature=shared}{Andrew Ng's YouTube playlist}, videos \#21 and from \#24 to \#30}