Machine Learning for Beginners

Hello / Bonjour / 你好 :)

Reference

[1] Stanford CS 229 Machine Learning Syllabus and Course Schedule
[2] Machine Learning by Andrew Ng
[3] Deep Learning by Andrew Ng
[4] Machine Learning and Reinforcement Learning in Finance Specialization
[5] Investment Management with Python and Machine Learning Specialization
[6] Machine Learning Engineering for Production (MLOps) Specialization

Math & Stat

Mathematics

MAT1400 Calculs [Class notes UdeM Winter 2020(in French) with Anne Bourlioux]

CS 229 Probability Review

CS229 Linear Algebra Review and Reference

Statistics

STT1700 Introduction to Statistics

STT2400 Linear Regression [Class notes UdeM Summer 2021(in English) with Melchior Basanze]

STT2700 Statistical concepts and methods[in French] with Kamel Belbahri

*Math&Stat Cheatsheet

Supervised Learning

Linear Regression

scikit-learn user guide: Linear Regression scikit-learn API: Linear Regression
Machine Learning Coursera (Andrew Ng): Ex1 Linear Regression
Machine Learning Coursera (Andrew Ng): Ex5 Bias Vs Variance

Movie Recommendation

Reference: Machine Learning Coursera (Andrew Ng) Ex8
Code: ex8_MovieRecommendation.html

Logistic Regression

scikit-learn user guide: Logistic Regression scikit-learn API: Logistic Regression
Machine Learning Coursera (Andrew Ng): Ex2 Logistic Regression
Machine Learning Coursera (Andrew Ng): Ex3 Multiclass Classification
Logistic Regression from scratch: sample code with sigmoid and softmax as activation function

SVM

scikit-learn user guide: SVM scikit-learn API: SVM scikit-learn Tips on Practical Use: SVM
scikit-learn Kernal functions: SVM Kernal functions
SVM Mathematical formulation: SVM-Mathematical-formulation.pdf
CS229 SVM notes: CS229-SVM.pdf

SVM - SVC

scikit-learn user guide: SVC
scikit-learn API: Linear Support Vector Classification: LinearSVC C-Support Vector Classification: SVC
Machine Learning Coursera (Andrew Ng): Ex6 SVM ( reference )
Machine Learning Coursera (Andrew Ng): Ex6 E-Mail Spam Classification Filter ( Code made by kaleko: reference )

Implement Kernal SVM from scratch

Reference: Devoir 1 STT3795 @UdeM avec Guy Wolf [in French]
Code: Implement_Kernal_SVM_from_scratch.html
Explanation: explanation.pdf

SVM - SVR

scikit-learn user guide: SVR
scikit-learn API: Linear Support Vector Regression: LinearSVR Epsilon-Support Vector Regression: SVR

Naive Bayes

scikit-learn user guide: Naive Bayes scikit-learn API: sklearn.naive_bayes
CS229 Naive Bayes notes: naive-bayes.pdf

Beyasian Network

Decision Tree

scikit-learn user guide: Decision Trees scikit-learn API: sklearn.tree: Decision Trees
CS 229 Decision Tree notes: decision-tree.pdf

Random Forest

scikit-learn user guide: Ensemble methods , Random Forest
scikit-learn API: sklearn.ensemble: Ensemble Method , Random Forest Classifier , Random Forest Regressor
Random Forest Hyperparameter tuning example: sample code

Nearest Neighbors

scikit-learn user guide: Nearest Neighbors , scikit-learn API: sklearn.neighbors: Nearest Neighbors , KNeighborsClassifier , KNeighborsRegressor
Imputation for completing missing values using k-Nearest Neighbors: sklearn.impute.KNNImputer
KNN Hyperparameter tuning and model comparison example: sample code

Unsupervised Learning

Decomposition

PCA

scikit-learn user guide: Principal component analysis (PCA) , scikit-learn API: sklearn.decomposition.PCA
CS 229 Principal component analysis (PCA) notes: pca.pdf
Machine Learning Coursera (Andrew Ng): Ex7 PCA
PCA in finance: PCA Eigen Portfolio , Absoption Ratio via PCA ( Reference )
CS 229 Independent Components Analysis (ICA) notes: ica.pdf

Clustering

scikit-learn user guide: Clustering , scikit-learn API: sklearn.cluster: Clustering

K-means

scikit-learn user guide: K-Means , scikit-learn API: sklearn.cluster.KMeans
Machine Learning Coursera (Andrew Ng): Ex7 K-means
CS229 K-means notes: Kmeans.pdf

DBScan

scikit-learn user guide: DBScan , scikit-learn API: sklearn.cluster.DBSCAN

Linkage

scikit-learn - Comparing different hierarchical linkage methods on toy datasets: hierarchical linkage methods
SciPy API: scipy.cluster.hierarchy.linkage

BIRCH

scikit-learn user guide: BIRCH , scikit-learn API: sklearn.cluster.Birch

Manifold Learning

scikit-learn user guide: Manifold learning , scikit-learn API: sklearn.manifold: Manifold Learning

MDS & Isomap

scikit-learn user guide: Isomap , Multi-dimensional Scaling (MDS)
scikit-learn API: sklearn.manifold.Isomap sklearn.manifold.MDS

Python visualisation exercice with MDS and Isomap

Reference: Devoir 2 STT3795 @UdeM avec Guy Wolf [in French]
Code: MDS_Isomap_Ex.html

Mixtures of Gaussians and the EM algorithm

Ensemble

Adaboost

Gradient Tree Boosting

LightGBM

XGBoost

Voting

Stacking

Neural Network

Forword Propagation

Machine Learning Coursera (Andrew Ng): Ex3 Neural Networks Forward Propagation
Deep Learning Coursera (Andrew Ng):
Forward Propagation

Back Propagation

Machine Learning Coursera (Andrew Ng): Ex4 Neural Networks Back Propagation
Deep Learning Coursera (Andrew Ng):
Backward Propagation

CNN

RNN

Advanced Topics

Natural Language Processing

Kaggle Sentiment Analysis Tweets

Self-Supervised Learning

Reinforcement Learning

Applications

Numpy, Pandas, Scikit-learn

Basic Pandas

Exploratory Data Analysis (EDA) and Visualisation

Model Selection in general

Feature engineering / Feature selection

Hyperparameter tuning

Metric and Scoring

Data Preprocessing

Application in finance

Pytorch

MLOps with TensorFlow