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docs(numpy-mini-project): fix typos and grammar in mean normalization notebook #18

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14 changes: 7 additions & 7 deletions NumPy Mini-Project/Mean Normalization and Data Separation.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
"\n",
"In machine learning we use large amounts of data to train our models. Some machine learning algorithms may require that the data is *normalized* in order to work correctly. The idea of normalization, also known as *feature scaling*, is to ensure that all the data is on a similar scale, *i.e.* that all the data takes on a similar range of values. For example, we might have a dataset that has values between 0 and 5,000. By normalizing the data we can make the range of values be between 0 and 1.\n",
"\n",
"In this lab, you will be performing a different kind of feature scaling known as *mean normalization*. Mean normalization will scale the data, but instead of making the values be between 0 and 1, it will distribute the values evenly in some small interval around zero. For example, if we have a dataset that has values between 0 and 5,000, after mean normalization the range of values will be distributed in some small range around 0, for example between -3 to 3. Because the range of values are distributed evenly around zero, this guarantees that the average (mean) of all elements will be zero. Therefore, when you perform *mean normalization* your data will not only be scaled but it will also have an average of zero. \n",
"In this lab, you will be performing a different kind of feature scaling known as *mean normalization*. Mean normalization will scale the data, but instead of making the values be between 0 and 1, it will distribute the values evenly in some small interval around zero. For example, if we have a dataset that has values between 0 and 5,000, after mean normalization the range of values will be distributed in some small range around 0, for example between -3 and 3. Because the range of values are distributed evenly around zero, this guarantees that the average (mean) of all elements will be zero. Therefore, when you perform *mean normalization* your data will not only be scaled but it will also have an average of zero. \n",
"\n",
"# To Do:\n",
"\n",
Expand All @@ -34,11 +34,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that you created the array we will mean normalize it. We will perform mean normalization using the following equation:\n",
"Now that you have created the array we will mean normalize it. We will perform mean normalization using the following equation:\n",
"\n",
"$\\mbox{Norm_Col}_i = \\frac{\\mbox{Col}_i - \\mu_i}{\\sigma_i}$\n",
"\n",
"where $\\mbox{Col}_i$ is the $i$th column of $X$, $\\mu_i$ is average of the values in the $i$th column of $X$, and $\\sigma_i$ is the standard deviation of the values in the $i$th column of $X$. In other words, mean normalization is performed by subtracting from each column of $X$ the average of its values, and then by dividing by the standard deviation of its values. In the space below, you will first calculate the average and standard deviation of each column of $X$. "
"where $\\mbox{Col}_i$ is the $i$th column of $X$, $\\mu_i$ is the average of the values in the $i$th column of $X$, and $\\sigma_i$ is the standard deviation of the values in the $i$th column of $X$. In other words, mean normalization is performed by subtracting from each column of $X$ the average of its values, and then by dividing by the standard deviation of its values. In the space below, you will first calculate the average and standard deviation of each column of $X$. "
]
},
{
Expand Down Expand Up @@ -94,7 +94,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"If you have performed the mean normalization correctly, then the average of all the elements in $X_{\\tiny{\\mbox{norm}}}$ should be close to zero, and they should be evenly distributed in some small interval around zero. You can verify this by filing the code below:"
"If you have performed the mean normalization correctly, then the average of all the elements in $X_{\\tiny{\\mbox{norm}}}$ should be close to zero, and they should be evenly distributed in some small interval around zero. You can verify this by filling the code below:"
]
},
{
Expand All @@ -120,7 +120,7 @@
"\n",
"# Data Separation\n",
"\n",
"After the data has been mean normalized, it is customary in machine learnig to split our dataset into three sets:\n",
"After the data has been mean normalized, it is customary in machine learning to split our dataset into three sets:\n",
"\n",
"1. A Training Set\n",
"2. A Cross Validation Set\n",
Expand Down Expand Up @@ -166,7 +166,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you can create the three datasets using the `row_indices` ndarray to select the rows that will go into each dataset. Rememeber that the Training Set contains 60% of the data, the Cross Validation Set contains 20% of the data, and the Test Set contains 20% of the data. Each set requires just one line of code to create. Fill in the code below"
"Now you can create the three datasets using the `row_indices` ndarray to select the rows that will go into each dataset. Remember that the Training Set contains 60% of the data, the Cross Validation Set contains 20% of the data, and the Test Set contains 20% of the data. Each set requires just one line of code to create. Fill in the code below"
]
},
{
Expand All @@ -193,7 +193,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"If you performed the above calculations correctly, then `X_tain` should have 600 rows and 20 columns, `X_crossVal` should have 200 rows and 20 columns, and `X_test` should have 200 rows and 20 columns. You can verify this by filling the code below:"
"If you performed the above calculations correctly, then `X_train` should have 600 rows and 20 columns, `X_crossVal` should have 200 rows and 20 columns, and `X_test` should have 200 rows and 20 columns. You can verify this by filling the code below:"
]
},
{
Expand Down
14 changes: 7 additions & 7 deletions NumPy Mini-Project/Solution.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
"\n",
"In machine learning we use large amounts of data to train our models. Some machine learning algorithms may require that the data is *normalized* in order to work correctly. The idea of normalization, also known as *feature scaling*, is to ensure that all the data is on a similar scale, *i.e.* that all the data takes on a similar range of values. For example, we might have a dataset that has values between 0 and 5,000. By normalizing the data we can make the range of values be between 0 and 1.\n",
"\n",
"In this lab, you will be performing a different kind of feature scaling known as *mean normalization*. Mean normalization will scale the data, but instead of making the values be between 0 and 1, it will distribute the values evenly in some small interval around zero. For example, if we have a dataset that has values between 0 and 5,000, after mean normalization the range of values will be distributed in some small range around 0, for example between -3 to 3. Because the range of values are distributed evenly around zero, this guarantees that the average (mean) of all elements will be zero. Therefore, when you perform *mean normalization* your data will not only be scaled but it will also have an average of zero. \n",
"In this lab, you will be performing a different kind of feature scaling known as *mean normalization*. Mean normalization will scale the data, but instead of making the values be between 0 and 1, it will distribute the values evenly in some small interval around zero. For example, if we have a dataset that has values between 0 and 5,000, after mean normalization the range of values will be distributed in some small range around 0, for example between -3 and 3. Because the range of values are distributed evenly around zero, this guarantees that the average (mean) of all elements will be zero. Therefore, when you perform *mean normalization* your data will not only be scaled but it will also have an average of zero. \n",
"\n",
"# To Do:\n",
"\n",
Expand Down Expand Up @@ -43,11 +43,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that you created the array we will mean normalize it. We will perform mean normalization using the following equation:\n",
"Now that you have created the array we will mean normalize it. We will perform mean normalization using the following equation:\n",
"\n",
"$\\mbox{Norm_Col}_i = \\frac{\\mbox{Col}_i - \\mu_i}{\\sigma_i}$\n",
"\n",
"where $\\mbox{Col}_i$ is the $i$th column of $X$, $\\mu_i$ is average of the values in the $i$th column of $X$, and $\\sigma_i$ is the standard deviation of the values in the $i$th column of $X$. In other words, mean normalization is performed by subtracting from each column of $X$ the average of its values, and then by dividing by the standard deviation of its values. In the space below, you will first calculate the average and standard deviation of each column of $X$. "
"where $\\mbox{Col}_i$ is the $i$th column of $X$, $\\mu_i$ is the average of the values in the $i$th column of $X$, and $\\sigma_i$ is the standard deviation of the values in the $i$th column of $X$. In other words, mean normalization is performed by subtracting from each column of $X$ the average of its values, and then by dividing by the standard deviation of its values. In the space below, you will first calculate the average and standard deviation of each column of $X$. "
]
},
{
Expand Down Expand Up @@ -113,7 +113,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"If you have performed the mean normalization correctly, then the average of all the elements in $X_{\\tiny{\\mbox{norm}}}$ should be close to zero, and they should be evenly distributed in some small interval around zero. You can verify this by filing the code below:"
"If you have performed the mean normalization correctly, then the average of all the elements in $X_{\\tiny{\\mbox{norm}}}$ should be close to zero, and they should be evenly distributed in some small interval around zero. You can verify this by filling the code below:"
]
},
{
Expand Down Expand Up @@ -163,7 +163,7 @@
"\n",
"# Data Separation\n",
"\n",
"After the data has been mean normalized, it is customary in machine learnig to split our dataset into three sets:\n",
"After the data has been mean normalized, it is customary in machine learning to split our dataset into three sets:\n",
"\n",
"1. A Training Set\n",
"2. A Cross Validation Set\n",
Expand Down Expand Up @@ -220,7 +220,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you can create the three datasets using the `row_indices` ndarray to select the rows that will go into each dataset. Rememeber that the Training Set contains 60% of the data, the Cross Validation Set contains 20% of the data, and the Test Set contains 20% of the data. Each set requires just one line of code to create. Fill in the code below"
"Now you can create the three datasets using the `row_indices` ndarray to select the rows that will go into each dataset. Remember that the Training Set contains 60% of the data, the Cross Validation Set contains 20% of the data, and the Test Set contains 20% of the data. Each set requires just one line of code to create. Fill in the code below"
]
},
{
Expand Down Expand Up @@ -259,7 +259,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"If you performed the above calculations correctly, then `X_tain` should have 600 rows and 20 columns, `X_crossVal` should have 200 rows and 20 columns, and `X_test` should have 200 rows and 20 columns. You can verify this by filling the code below:"
"If you performed the above calculations correctly, then `X_train` should have 600 rows and 20 columns, `X_crossVal` should have 200 rows and 20 columns, and `X_test` should have 200 rows and 20 columns. You can verify this by filling the code below:"
]
},
{
Expand Down