Test Date: September 3, 2024

Total Score: 314

Verbal Score: 152 (48th Percentile)

Quant Score: 162 (60th Percentile)

AWA Score: 3.5 (41st Percentile)

Test Date: September 9, 2023

Listening Score : 8.5

Reading Score : 7.5

Speaking Score : 7.0

Writing Score : 6.5

Learnt the fundamental ideas of Hypothesis, Cost function, Gradient Descent, Supervised and Unsupervised Learning, Activation Functions

Learnt many fundamental Algorithms such as Logistic Regression, Support Vector Machine, Anamoly detections e.t.c.

Implemented the algorithms using Python and Octave

Understood Neural Networks from ground up and implemented it from scratch using only numpy array of Python

Deeply studied the Backpropagation process and understood how it worked to reduce cost functions, also implemented it in Python

Understood the importance of hyperparameters and how they should be manipulated in certain scenarios

Learnt differnet techniques like, Regularization, Dropout etc. to avoid overfitting a model in a dataset.

Understood the importance of visualization of charts inorder to take correct decisions during the implementation pahase of a project. It can help to build successful models

Learnt the basic building blocks of CNN, such as filters, stride, padding etc. and also implemented it from scratch with Python

Learnt differnet CNN architectures, such as Inception Networks, VGG - 16, Resnet etc

Learnt the YOLO object detection algorithm from the basics, including background ideas such as sliding window technique, anchor boxes etc.