LunarTech.ai

Introduction

Lecture 1: Machine Learning Basics

Lecture 2: Bias-Variance Trade-off

Lecture 3: Overfitting & Regularization

Lecture 4.1: Causal Analysis & Linear Regression (from Fundamentals to Statistics)

Lecture 4.2: Linear Regression & Ordinary Least Squares (OLS)

Lecture 5: Logistic Regression & Maximum Likelihood Estimation (MLE)

Lecture 6: Linear Discriminant Analysis (LDA)

Lecture 7: K-Nearest Neighbors (KNN)

Lecture 8: Decision Trees

Lecture 9: Bagging

Lecture 10: Random Forest

Lecture 11: Boosting (Part 1) Introduction

Lecture 12: Boosting (Part 2) - AdaBoost

Lecture 13: Boosting (Part 3) - Gradient Boosting Model (GBM)

Lecture 14: Boosting (Part 4) - XGBoost

Lecture 15: Clustering (Part 1) - K-Means & Elbow Method

Lecture 16: Clustering (Part 2) - Hierarchical Clustering

Lecture 17: Clustering (Part 3) - DBSCAN

Lecture 18: Dimensionality Reduction (Part 1) - Feature Selection

Lecture 19: Dimensionality Reduction (Part 2) - Principal Component Analysis

Lecture 20: Resampling Techniques (Part 1) - Cross Validation

Lecture 21: Resampling Techniques (Part 2) - Bootstrapping

Lecture 22: Optimization Techniques - Grid-Search, GD, SGD, SGD-Momentum, Adam

Fundamentals to Machine Learning Quiz

ML Interview Preparation E-book Guide

Ebook: Machine Learning Interview Guide

Extra: Data Science Interview Content and Preparation

Free Resources Guide

Free Resources for Fundamentals to Machine Learning