IIT Madras MLP Lectures: Machine Learning Practice
BSCS200898 lectures11 weeks
Weeks
- Week 16 lecturesPandas series & dataframe | part 1 · Pandas series & dataframe | part 2 · Data selection using pandas | part 1 · Data selection using pandas | part 2 · Drop in dataframe · Apply statistics using pandas
- Week 213 lecturesIntroduction to scikit-learn · Data loading · Demonstration of sklearn dataset api · Data preprocessing · Handling missing data · Categorical transformers · Numeric transformers · Feature scaling · Outliers · Filter based feature selection · Wrapper based feature selection · Heterogeneous features transformations · Dimensionality reduction by PCA
- Week 34 lecturesChaining transformers · Demonstration of data extraction, imputation, scaling, visualizing feature distribution · Demonstration of Data transformation, composite transformers · Demonstration of feature selection demonstration, PCA, pipelines, handling class imbalance
- Week 413 lecturesLinear regression · Model evaluation · Polynomial regression · Regularization · Hyper parameter tuning · Multi Learning Classification · Naive bayes classifier · K nearest neighbours · Support vector machines in scikit-learn · Decision trees · Voting, bagging & random forest · Boosting: adaboost, gradient boosting · Neural networks: multi-layer perceptron(mlp)
- Week 510 lecturesExplore california housing dataset · Linear regression on california housing dataset · Linear regression demonstration · Baseline model · SGDregressor demonstration · Demonstration: K-NN with california housing dataset · Decision trees for regression · Bagging & random forest regressor on california housing dataset · Adaboost & gradient Boost regressor on california housing · MLP regressor on california housing dataset
- Week 617 lecturesClassification functions in scikit learn · Evaluating classifiers · Demonstration: binary class image classification with perceptron · Demonstration: multi class image classification with perceptron · Demonstration: MNIST digits classification using SGDregressor · Demonstration: MNIST digits classification using logistic regression · Demonstration: Zero detector with ridge classifier · Demonstration: multiclass classifier on MNIST dataset · Demonstration: naive bayes classifier · Demonstration: softmax regression with MNIST · Demonstration: KNN with MNIST · Demonstration: SVC on MNIST dataset · Decision trees for classification - abalone · Decision trees for classification - iris · Bagging & random forest classifier on MNIST · Adaboost & gradientboost classifier on MNIST · MLP classifier on MNIST dataset
- Week 77 lecturesLarge scale machine learning · Confusion matrix · Accuracy · Precision · F1 score · Recall · Multiclass
- Week 97 lecturesK-means clustering on digit dataset · HAC demo · Introduction: looking at the big picture · Data visualization · Data preparation · Selection & training of machine learning models · Finetuning machine learning models
- Week 107 lecturesRecommender Systems · Movie Lens EDA · Content Based Filtering · Content Based Filtering Example 2 · item - item Collaborative Filtering · user - user collaborative filtering · Matrix Factorization
- Week 116 lecturesTime series using pandas · Time Resampling · Classical Decomposition · STL Decomposition · Stationarity · Make data stationary
- Week 124 lecturesWhat is transfer learning · Practical implementation of transfer learning · Quick recap of neural networks · Transfer learning theoretical implementation