Part II : Deep Learning
Introduction to NLP :
NLP1.pdf. Companion notebook : NLP1.ipynb
Introduction to Neural Networks :
Part I : Classical ML
Course 5 : advanced supervised learning
Lecture 5 on conformal prediction : Lecture5.pdf
Notebooks : ComputerExample.ipynb, Lecture5.ipynb, QuantileRegression.ipynb and ConformalPrediction. ipynb
Datasets : diamonds.csv
Lab 5 : Lab5.pdf. Dataset : dataset.csv
Course 4 : Basics on supervised learning
Overview of supervised learning : OverviewSupervised.pdf
Two websites about regression : Vanilla linear regression and Ridge-vs-Lasso
Linear regression with sklearn : website. Dataset : bottle.csv. Notebook : VanillaLinearRegression.ipynb
A website on Logistic regression. Python implementation . Notebook : LogisticRegression.ipynb
More on decision trees. Two Python examples : classification trees and regression trees
Datasets : balance-scale.csv.
Notebook for classification trees: ClassificationTree.ipynb
Evaluation : ConfusionMatrix
Notebook for regression trees : RegressionTree.ipynb
Random Forest in Python : this website with the dataset PositionSalaries.csv
Notebook for Random Forest : RandomForest.ipynb
Evaluation in regression : oob-score , R2. More details here
Lab3 : Lab3.pdf
More on feature importance with Randome Forest : this website
Course 3: Basics on clustering
Overview of unsupervised learning : Overview-Clustering.pdf
Lecture on clustering : Clustering.pdf. Notebook : Clustering. ipynb
The scikit-learn website
Lab on clustering : Lab.pdf
Dataset : Live.csv
Notebook : LabClustering.ipynb
Some websites about clustering
See Hierarchical Clustering and this Medium website for Hierachical Clustering
Course 2 : Basics on machine learning
Lecture 2 : OverviewML.pdf
Course 1 : Basics on Random Variables with Python
First lecture on Random Variables : CM_RandomVariables.pdf. Notebook of Lecture 1 : Lecture1.ipynb
Tutorial on Seaborn : Seaborn.ipynb. Iris dataset : iris.csv
Exploratory Data Analysis : EDA1.pdf. Data : Ames.csv. Notebook : EDA.ipynb