Part II : Introduction to deep learning
Course 9 : Introduction to GenAI
Introduction : Lecture9.pdf
Notebook for data generation : Lecture9-data-generation.ipynb
Data : products.csv, news.csv, wiki_stub.csv and sentiment.csv
Notebooks for prompting : Lecture9_data_to_text.ipynb, Lecture9_summarization.ipynb, Lecture9_full.ipynb
Course 8 : Seq2seq Neural Networks
Lecture 8 : Lecture8.pdf
An example detailed with calculation details
Notebook : Lecture8a.ipynb and Lecture8b.ipynb
Alternative approach : Lecture8c.ipynb
Datasets : calendar.csv, sales_train_evaluation.csv, sell_prices.csv
Lab 8 : Lab8.pdf
Dataset : PJME_hourly.csv
Course 7 : Basics on Neural Networks
Lecture 7 : Lecture7.pdf.
The Feedforward Neural Network and its learning using Backpropagation
Filters in CNN
Notebook : Lecture7.ipynb. Image : image_before.png
Course 6 : Basics in Optimisation
Lecture 6 : Lecture6a.pdf, Lecture6b.pdf
Notebooks : Lecture6a.ipynb, Lecture6b.ipynb, Lecture6c.ipynb, Lecture6d.ipynb
Labs : Lab6.pdf. Notebook : Lab6.ipynb
Part I : Vanilla machine learning for tabular data
Course 5 : advanced supervised learning
Lecture 5 on conformal prediction : Lecture5.pdf
Notebooks : QuantileRegression.ipynb and Lecture5.ipynb
Datasets : diamonds.csv
Practical Session 5
Lab 5 : Lab5.pdf. Dataset : dataset.csv
Notebook for Lab5 : Lab5.ipynb
Course 4 : Basics on supervised learning
Overview of supervised learning : OverviewSupervised.pdf
Some websites related to Course 4
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
More on feature importance with Random Forest : this website
Practical Session 4
Lab4 : Lab4.pdf
Notebook of Lab 4 : Lab4.ipynb
Course 3: Basics on clustering
Overview of unsupervised learning : Overview-Clustering.pdf
The scikit-learn website
Lab on clustering : Lab.pdf
Dataset : Live.csv
Notebook : LabClustering.ipynb
Some websites about clustering
More on KMeans
See Hierarchical Clustering and this Medium website for Hierachical Clustering
Course 2 : Basics on machine learning
Lecture 2 : OverviewML.pdf
EDA on Tips Dataset
A review of clustering algorithms
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
Notebook : EDA.ipynb