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Test PR #1

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amit1rrr May 4, 2020
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amit1rrr May 4, 2020
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amit1rrr May 4, 2020
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change 04.02
amit1rrr Apr 11, 2021
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4 changes: 2 additions & 2 deletions notebooks/00.00-Preface.ipynb
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"cell_type": "markdown",
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@komaljack komaljack Jan 18, 2023 •
edited by amit1rrr
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QWERTY--comment-edited-VIA-code

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@komaljack komaljack Jan 18, 2023 •
edited by amit1rrr
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another-QWERTY-comment-edited-VIA-code

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@komaljack komaljack Jan 20, 2023

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review comment 9


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review comment 4


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reply - 1

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review comment 11


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review comment 7


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review comment 8


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"metadata": {},
"source": [
"Let's see if we can create code block in md -- \n",
"A Let's see if we can create code block in md -- \n",
"\n",
" book a = 2 about doing data science\n",
" which immediately begs the question\n",
" what is data science"
"You know what is data science"
]
},
{
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32 changes: 9 additions & 23 deletions notebooks/04.02-Simple-Scatter-Plots.ipynb
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Expand Up @@ -62,9 +62,7 @@
{
"cell_type": "code",
"execution_count": 2,
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comment - def

"metadata": {
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@komaljack komaljack Jan 18, 2023 •
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comment - abc fsfsd -- v2

"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -94,9 +92,7 @@
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -128,9 +124,7 @@
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand All @@ -157,9 +151,7 @@
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -201,9 +193,7 @@
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -233,9 +223,7 @@
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -273,9 +261,7 @@
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
Expand Down Expand Up @@ -345,9 +331,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
"nbformat_minor": 1
}
2 changes: 1 addition & 1 deletion notebooks/05.09-Principal-Component-Analysis.ipynb
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Expand Up @@ -29,7 +29,7 @@
"cell_type": "markdown",
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Line #2.    X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T

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Line #8.    # plot data

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Line #2.    pca.fit(X)

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Line #3.    X_pca = pca.transform(X)

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Line #5.    print("transformed shape:", X_pca.shape)

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Line #1.    from sklearn.datasets import load_digits

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Line #1.    pca = PCA(2)  # project from 64 to 2 dimensions

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Line #4.    plt.ylabel('cumulative explained variance');

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@komaljack komaljack Jan 20, 2023 •
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qwerty1


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"metadata": {},
"source": [
"# In Depth: Principal Component Analysis"
"# In Depth: New Component Analysis"
]
},
{
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