esviswajith95 / MLP

Notebooks from ML Practice course at IITM Online degree programme

MLP - Class notebooks

This is a collection of Jupyter notebooks I prepared while attending Machine Learning Practice Course from IIT Madras Online Degree Programme in Data Science and Programming(Diploma Level).

The course focuses on practical implementation of machine learning algorithm using scikit-learn APIs.

Running Examples

• California housing prediction for regression tasks.
• MNIST Digit recognition for classification tasks

Table of contents

Week 1

• Data loading
  • Basic data loading/generation features(load, fetch, make)

Week 2

• Data preprocessing
  • Data cleaning
    • Feature Exraction - DictVectorizer
    • Data Imputation - SimpleImputer, KNNImputer
    • Feature Scaling - MaxAbsoluteScaler, MinMaxScaler, StandardScaler,
  • Feature transformations
    • Polynomial Features - PolynomialFeatures
    • Discretization - KBinsDiscretizer
    • Handling categorical variables - OrdinalEncoder, OneHotEncoder, LabelEncoder, MultiLabelBinarizer, pandas.get_dummies, add_dummy_feature
    • Custom Transformers - FunctionTransformer
    • Composite Transformers - ColumnTransformer, TransformedTargetRegressor
  • Feature Selection
    • Filter based methods - VarianceThreshold, SelectKBest, SelectPercentile, GenericUnivariateSelect
    • Wrapper based Methods - RFE, SelectFromModel, SequentialFeatureSelector
  • Feature extraction
    • PCA - PCA
  • Pipeline - Pipeline, make_pipeline, FeatureUnion
  • Hyper Parameter tuning and Cross validation - GridSearchCV, RandomizedSearchCV
  • Handling imbalance(imblearn) - RandomUnderSampler, RandomOverSampler, SMOTE

Week 3

• Baseline models
  • How to build simple baseline models
• Linear Regression
  • Normal equation method(LinearRegression)
  • Iterative optimisation method(SGDRegressor)

Week 4

• California Housing Prediction
  • Exploratory data Analysis
  • Regularised Linear regression and Hyper parameter tuning

Week 5

• Perceptron
  • Binary Classification
  • Multiclass Classification

Week 6

• Logistic regression
• Naive Bayes models

Week 7

• K Nearest Neighbour model
  • Classification
  • Regression
• Training Large Scale ML Models
  • Learning in batches(partial_fit())

Week 8

• Softmax Regression
• Support Vector Machines

Week 9

• Decision trees

Week 10

• Bagging and Boosting
• RandomForest

Week 11

• Clustering
  • K Means
  • Heirarchical Agglomerative Clustering

Week 12

• Multi-layer Perceptron