README.md

Machine Learning

This section presents a list of resources aimed at those who want to start or continue their Machine Learning journey!

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Table of contents

• Learning
• Machine Learning Algorithms from scratch
• General Machine Learning
• Supervised Learning
  • Algorithms for both Classification and Regression
  • Regression
  • Classification
• Unsupervised Learning
  • Dimensionality Reduction
  • Clustering
• Interview Questions
• Blogs to follow
• Suggested books
• Where to find datasets
• Datasets

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Free Learning Materials

• StatQuest with Josh Starmer (bam!)
• ML Engineer - Step by step guide for beginners
• Kaggle Learning Couses: ML, Data Visualization, Computer Vision, NLP
• Stanford CS229: Machine Learning
• Fast.ai

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Machine Learning Algorithms from scratch

• K Nearest Neighbors : code, article

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General Machine Learning

• AI vs ML
• DL vs ML
• Data Mining vs ML
• 14 Different Types of Learning in Machine Learning
• Bias in Machine Learning
• Overfitting and Underfitting
• Bias Variance trade-off
• Data Imbalance
• Ensemble Learning: Bagging, Boosting & Stacking
• Feature Selection
  • Forward selection and Backward elimination
  • Recursive Feature Elimination (RFE)
  • Feature Importance

Supervised Learning

• Empirical Risk Minimization paradigm
• Regression Versus Classification
• Evaluating a Machine Learning Model

Algorithms for both Classification and Regression

• Decision Trees and Random Forests
• Introduction to Support Vector Machines (SVM)
• K Nearest Neighbors

Regression

• Linear Regression
• Ridge and Lasso Regression
• Multivariate Regression Analysis

Classification

• Naive Bayes Classifier
• Logistic Regression
• Artificial Neural Network

Unsupervised Learning

• What is unsupervised learning?

Dimensionality Reduction

• Curse of Dimensionality
• Principal Component Analysis (PCA)
• Singular Value Decomposition (SVD)
• Linear Discriminant Analysis (LDA)

Clustering

• What is clustering
• Hierchical clustering : Agglomerative and Divisive
• Partitional clustering & K-Means algorithm
• K-Means vs Mini Batch K-Means
• DBSCAN
• Spectral Clustering

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Common Interview Questions for ML Enginners

• Foundamental Questions
• Foundamentals of ML and questions about you correlated to ML
• General ML Engineer roles questions - Glassdor

Suggested Books

• Theory
  • DeepLearning book
  • An Introduction to Statistical Learning V2
  • Mathematics for Machine Learning
  • Interpretable Machine Learning
• Interview Preparation
  • Chip Huyen - Machine Learning Interviews Book
  • Cracking the Machine Learning Interview - English Version

Blogs to follow

• Towards Data Science
• Machine Learning Mastery
• DeepMind
• Cassie Kozykrov
• Eugene Yan
• Jeremy Jordan

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Where to find datasets:

• Kaggle
• UCI Machine Learning Repository
• Data World
• European Union Open Data Portal
• DataHub

Datasets

Name	Task	Host	Link
Heart Failure Prediction Dataset	classification	Kaggle	🔗
New York City Airbnb Open Data	regression	Kaggle	🔗
Credit Card Fraud Detection	classification	Kaggle	🔗
Wine Quality	regression classification	UCI	🔗
Forest Fires	regression	UCI	🔗