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MastAI ki paathSHALA : Data Science, Machine Learning, and Deep Learning

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SHALA 2020 (DS,ML)

Course on Data Science, Machine Learning, Deep Learning (MastAI ki paathSHALA)

(Index Page: Hritik Jaiswal) Referred and tried to make like his repo and index page borrowed from his repo.

Topic

Assignment Content
1 Getting started : Python data structure, Loops, Classes, Linear Algebra
2 Basic data understanding: Data science, Central tendency, Plots, Cumulative distribution
3 Improving plots: :Different types of plots, How to customize plots
4 Basic statistics : Maximum likelihood estimation, sufficient statistics, null hypothesis testing, t-test, Wilcoxon rank test
5 Introduction to ML : Machine learning problems, parameter vs. hyperparameter, overfitting, training, validation, testing, cross-validation, regularization
6 Decision Trees : Definition of a decision tree, metrics of impurity, greedy algorithm to split a node, tree depth and pruning, ensemble of trees (random forest)
7 Bayesian decision theory : Bayes rule: Prior, likelihood, posterior, evidence, Gaussian density, sufficient statistics, maximum likelihood derivation for mean and covariance
8 Linear models : linear regression and its analytical solution, loss function, gradient descent and learning rate, logistic regression and its cost, SVM: hinge loss with L2 penalty
9 Kernelization: Dual form of an SVM, kernels for a dual form, examples of kernels and their typical uses, SVR in primal form, SVR in dual form
10 Feature selection and engineering : Normalization, text analysis, T-test, forward selection, features for images, features for audio, features for images, features for NLP, PCA, ZCA, K-PCA
11 Dense and shallow neural networks : Logistic regression as a sigmoid, single hidden layer using sigmoid and ReLU, approximation of any function using a single hidden layer, overfitting, advantage of multiple hidden layers, neural networks for regression, multi-regression, multi-classification using softmax, back propagation.
12 Advanced topics in neural networks: Weight initialization, momentum, weight decay, early stopping, batch SGD, advanced optimizers such as RMSprop and ADAM
13 Clustering: K-means, DB-SCAN, agglomerative clustering, scaling of dimensions, goodness of clustering
14 CNNs for Image classification: Applications of computer vision, implementation of convolution, building a convolutional neural network, image Classification using CNNs.

DataCamp Projects

(Image and this paragraph is borrowed from sudhakar's sir repo).

After finishing the ML/DL courses, I completed a few projects on DataCamp, as given below. These projects made me utilize both ML and DL skills using Python.