Academic Credentials
University Degrees
Master of Applied Science (Engineering), University of Windsor
Master of Business Administration (MBA), IGNOU
Bachelor of Engineering (BE), Gujarat University
Data Scientist in Python
Professional Licenses
Project Management Professional (P.M.P.), Project Management Institute (P.M.I.), USA
Professional Engineer (P.E.), Ohio State Board of Registration of Professional Engineers and Surveyors , USA
Data Science & Machine Learning Credentials
Machine Learning by Stanford University
Topics Covered (Coursera):
Supervised and Unsupervised Learning, Nearest Neighbors, Linear Regression, Logistic Regression, Neural Networks, Support Vector Machines (SVM), Clustering, Decision Tree Approach, Random Forest Model, Principal Component Analysis (PCA), Anomaly Detection, Recommender System, Handling Skewed Data, Large Scale Machine Learning
Practical Deep Learning for Coders
(Data Institute at USF)
Topics Covered (MOOC):
Identifying and Learning the Best Practices with Classification and Regression in Deep Learning.
Image Classifier, How to distinguish Different Kinds of Images, Structured Data (Sales Forecasting), NLP Classifier, Collaborative Filtering (Recommender System), Computer Vision (To find cat location in the picture), Build Own Architecture from Scratch
Convolutional Neural Network
Topics Covered (Coursera):
Computer Visions, Edge Detection, Padding, Strided Convolutions, Convolutions Over Volume, One layer of convolutional network, Pooling Layers, Classic Networks, ResNets, Networks in Networks and 1×1 Convolutions, Inception Network Motivation, Transfer Learning, Data Augmentation, State of Computer Vision, Object Localization, Landmark Detection, Object Detection, Bounding Box Predictions, Intersection Over Union (IOU), Non-max Suppression, Anchor Boxes, YOLO Algorithm, Region Proposals, Face Recognition, One Shot Learning, Siamese Network, Triplet Loss, Neural style transfer, Deep ConvNets Learning, Content Cost Function, Style Cost Function, 1D and 3D Generalizations
Sequence Models
Topics Covered (Coursera):
Recurrent Neural Network (RNN), Backpropagation through time, Different types of RNNs, Language Model and Sequence Generation, Sampling Novel Sequences, Vanishing Gradients with RNNs, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Bidirectional RNN (BRNN), Deep RNNs (DRNN), Word Representation, Word Embeddings, Embedding matrix, Word2Vec, Negative sampling, GloVe word vectors, Sentiment Classification, Debiasing Word Embeddings, Basic Models, Beam Search, Error Analysis in beam search, Bleu Score, Attention Model, Speech Recognition, Trigger Word Detection
Structuring Machine Learning Projects
Topics Covered (Coursera):
Orthogonalization, Single Number Evaluation Metric, Satisficing and Optimizing Metric, Avoidable Bias, Understanding human-level performance, Surpassing human-level performance, Improving model performance, Carrying out error analysis, Cleaning up incorrectly labeled data, Training and testing on different distributions, Addressing data mismatch, Transfer learning, Multi-task learning, End-to-End Deep Learning
Improving Deep Neural Networks
Topics Covered (Coursera):
Regularization, Dropout Regularization, Normalizing inputs, Vanishing / Exploding Gradients, Weight Initialization for Deep Networks, Numerical Approximation of Gradients, Gradient Checking, Mini-Batch Gradient Descent, Exponentially weighted averages & Bias Correction, Gradient Descent with Momentum, RMSprop, Adam Optimization Algorithm, Learning Rate Decay, The problem of local optima, Tuning Process, Hyperparameters, Normalizing activations in a network, Fitting Batch Norm into a neural network, Softmax Regression, Keras, TensorFlow
Neural Networks and Deep Learning
Topics Covered (Coursera):
Supervised Learning with Neural Network, Binary Classification, Logistic Regression, Logistic Regression Cost Function, Derivatives, Vectorizing Logistic Regression, Broadcasting in Python, Flaten a Matrix, Activation Functions, Gradient Descent for Neural Networks, Backpropagation Intuition, Random Initialization, Deep L-Layer Neural Network, Forward Propagation in Deep Network
Bayesian Statistics: From Concept to Data Analysis
by: University of California, Santa Crus
Topics Covered (Coursera):
Bayesian Statistics: Probability, Bayes Theorem, Review of Distributions, Frequentist Inference, Bayesian Inference, Priors, Bernoulli / Binomial Data, Poisson Data, Exponential Data, Normal Data, Linear Regression
Deep Learning with PyTorch
Topics Covered (Coursera):
PyTorch Fundamentals, Building Neural Network, Computer Vision – CNN for Digit Recognition, Sequence Models – RNN for Text Generation, Autoencoder – Denoising Images
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