IITGuwahati-AI · Gitanjit · Apr 4, 2020 · Apr 4, 2020 · Apr 4, 2020 · Apr 4, 2020
diff --git a/Phase 3 - 2020 (Summer)/README.md b/Phase 3 - 2020 (Summer)/README.md
@@ -10,7 +10,8 @@
 
 |Week   |Start Date        |Content                                            |End Date         |
 |-------|------------------|---------------------------------------------------|-----------------|
-| 1     | 29 Mar 2019      |**Python** + use of **matplotlib,numpy and pandas**| 4 Apr 2020      |
+| 1     | 29 Mar 2020      |**Python** + use of **matplotlib,numpy and pandas**| 4 Apr 2020      |
+| 2     | 05 Apr 2020      | ML Coursera Week 1 & 2                            | 11 Apr 2020     |
 
 > Will updated as time proceeds.
 

diff --git a/Phase 3 - 2020 (Summer)/WEEK4_exercise3_solutions.ipynb b/Phase 3 - 2020 (Summer)/WEEK4_exercise3_solutions.ipynb
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diff --git a/...020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/89.png b/...020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/89.png
diff --git a/...20 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/910.png b/...20 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/910.png
diff --git a/...2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/Answers_textual b/...2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/Answers_textual
@@ -0,0 +1,12 @@
+1.After comparing all the the bifeature graphs , on the basis of observation I have concluded that Feature 1 vs Feature 2 graph depicts 
+the labels in the best possible way as the two labels are nearly divided into 2 regions of concentric circles(1st quadrant only) centered
+at origin with radii 4 and 8 respectively
+2.PCA analysis
+Again feature pair 1,2 comes out to be the best pair as it distinguishes the labels in the best way . This conclusion has been arrived at in 2 ways
+a. In the first code block , the variance ratios have been compared after PCA reductionof feature pairs as well as feature pairs with labels
+-From the analysis it is concluded that features 3 and 8 retain the largest variance ratio when PCA-reduced from 2-d to 1-D
+That means that this feature pair is the most alike and hence redundant i.e. one feature can be expressed in the other's form  
+But when combined with the labels: 1,2 retains the highest variance ratio which means that it ,this pair, can depict the labels in the best possible way
+b.In the second code snippet the 1D reduced graphs of each pairs vs labels has been analysed at feature 1,2 pair comes out be the best pair again ,by observation
+From the analysis its clear from the figures that feature 1 and 2 pair distinguishes the labels most appropriately
+The second best pair seems to be 2,10 which matches with the first analysis made above {91.99 for feature 2,10 pair and 92.11 for 1,2 pair}
diff --git a/...0 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/PCA_analysis.ipynb b/...0 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/PCA_analysis.ipynb
diff --git a/... 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/Plotting.ipynb b/... 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/Plotting.ipynb
diff --git a/...e 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/plot_code b/...e 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/Learning-Content/Gitanjit_190123027/plot_code
@@ -0,0 +1,24 @@
+#this code has been written in Jupyter Notebook
+import numpy as np
+import pandas as pd
+data=np.loadtxt(fname='data_wk1')#extracting data from the data_wk1 file created in the notebook home(with the first non-data row removed)
+                                 #into a 2d numpy.ndarray data
+X=data[:,0]#labels 1-D array
+Y=data[:,1:11]#features 2-D array
+from matplotlib import pyplot as plt
+from matplotlib import style
+style.use('ggplot')
+for m in range(1,11):
+    for n in range(m+1,11):
+        y1=data[:,m]
+        y2=data[:,n]
+        for i in np.arange(999):
+            if X[i]==1.0 :
+                plt.scatter(y1[i], y2[i], color='red')#, align='center')
+            elif X[i]==2.0 :
+                plt.scatter(y1[i], y2[i], color='blue')#, align='center')
+        plt.title('Feature'+str(m)+' vs Feature'+str(n))
+        plt.ylabel('feature'+str(n))
+        plt.xlabel('feature'+str(m))
+        plt.show()      
+
diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/README.md b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/README.md
@@ -45,4 +45,4 @@ Follow this [GitHub Tutorial](https://towardsdatascience.com/getting-started-wit
 
 3. After **4 Apr 2020 EOD** no request will be entertained.
 
-4. For any serious doubt regarding code or data set create an issue in this very repository or comment on the FB post ***(of particular week)***with your doubts.
+4. For any serious doubt regarding code or data set create an issue in this very repository or comment on the FB post (***of particular week***)with your doubts.
diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Data/ex1data1.txt b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Data/ex1data1.txt
@@ -0,0 +1,97 @@
+6.1101,17.592
+5.5277,9.1302
+8.5186,13.662
+7.0032,11.854
+5.8598,6.8233
+8.3829,11.886
+7.4764,4.3483
+8.5781,12
+6.4862,6.5987
+5.0546,3.8166
+5.7107,3.2522
+14.164,15.505
+5.734,3.1551
+8.4084,7.2258
+5.6407,0.71618
+5.3794,3.5129
+6.3654,5.3048
+5.1301,0.56077
+6.4296,3.6518
+7.0708,5.3893
+6.1891,3.1386
+20.27,21.767
+5.4901,4.263
+6.3261,5.1875
+5.5649,3.0825
+18.945,22.638
+12.828,13.501
+10.957,7.0467
+13.176,14.692
+22.203,24.147
+5.2524,-1.22
+6.5894,5.9966
+9.2482,12.134
+5.8918,1.8495
+8.2111,6.5426
+7.9334,4.5623
+8.0959,4.1164
+5.6063,3.3928
+12.836,10.117
+6.3534,5.4974
+5.4069,0.55657
+6.8825,3.9115
+11.708,5.3854
+5.7737,2.4406
+7.8247,6.7318
+7.0931,1.0463
+5.0702,5.1337
+5.8014,1.844
+11.7,8.0043
+5.5416,1.0179
+7.5402,6.7504
+5.3077,1.8396
+7.4239,4.2885
+7.6031,4.9981
+6.3328,1.4233
+6.3589,-1.4211
+6.2742,2.4756
+5.6397,4.6042
+9.3102,3.9624
+9.4536,5.4141
+8.8254,5.1694
+5.1793,-0.74279
+21.279,17.929
+14.908,12.054
+18.959,17.054
+7.2182,4.8852
+8.2951,5.7442
+10.236,7.7754
+5.4994,1.0173
+20.341,20.992
+10.136,6.6799
+7.3345,4.0259
+6.0062,1.2784
+7.2259,3.3411
+5.0269,-2.6807
+6.5479,0.29678
+7.5386,3.8845
+5.0365,5.7014
+10.274,6.7526
+5.1077,2.0576
+5.7292,0.47953
+5.1884,0.20421
+6.3557,0.67861
+9.7687,7.5435
+6.5159,5.3436
+8.5172,4.2415
+9.1802,6.7981
+6.002,0.92695
+5.5204,0.152
+5.0594,2.8214
+5.7077,1.8451
+7.6366,4.2959
+5.8707,7.2029
+5.3054,1.9869
+8.2934,0.14454
+13.394,9.0551
+5.4369,0.61705
diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Data/ex1data2.txt b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Data/ex1data2.txt
@@ -0,0 +1,47 @@
+2104,3,399900
+1600,3,329900
+2400,3,369000
+1416,2,232000
+3000,4,539900
+1985,4,299900
+1534,3,314900
+1427,3,198999
+1380,3,212000
+1494,3,242500
+1940,4,239999
+2000,3,347000
+1890,3,329999
+4478,5,699900
+1268,3,259900
+2300,4,449900
+1320,2,299900
+1236,3,199900
+2609,4,499998
+3031,4,599000
+1767,3,252900
+1888,2,255000
+1604,3,242900
+1962,4,259900
+3890,3,573900
+1100,3,249900
+1458,3,464500
+2526,3,469000
+2200,3,475000
+2637,3,299900
+1839,2,349900
+1000,1,169900
+2040,4,314900
+3137,3,579900
+1811,4,285900
+1437,3,249900
+1239,3,229900
+2132,4,345000
+4215,4,549000
+2162,4,287000
+1664,2,368500
+2238,3,329900
+2567,4,314000
+1200,3,299000
+852,2,179900
+1852,4,299900
+1203,3,239500
diff --git a/...e 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Figures/cost_function.png b/...e 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Figures/cost_function.png
diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Figures/dataset1.png b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Figures/dataset1.png
diff --git a/...e 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Figures/learning_rate.png b/...e 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Figures/learning_rate.png
diff --git a/...- 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Figures/regression_result.png b/...- 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/Figures/regression_result.png