About half a year ago, I organized all my deep learning-related videos in a handy blog post to have everything in one place.

Since many people liked this post, and because I like to use my winter break to get organized, I thought I could free two birds with one key by compiling this list below.

Here, you find a list of approximately 90 machine learning lectures I recorded in 2020 and 2021! Once again, I hope this is useful to you!

PS: Of course, all code examples are in Python :)

Table of Contents

• Part 1: Introduction
  • L01 - Course overview, introduction to machine learning
  • L02 - Introduction to Supervised Learning and k-Nearest Neighbors Classifiers
• Part 2: Computational foundations
  • L03 - Using Python
  • L04 - Introduction to Python’s scientific computing stack
  • L05 - Data preprocessing and machine learning with scikit-learn
• Part 3: Tree-based methods
  • L06 - Decision trees
  • L07 - Ensemble methods
• Part 4: Model evaluation
  • L08 - Model evaluation 1 – overfitting
  • L09 - Model evaluation 2 – confidence intervals
  • L10 - Model evaluation 3 – cross-validation and model selection
  • L11 - Model evaluation 4 – algorithm selection
  • L12 - Model evaluation 5 – evaluation and performance metrics
• Part 5: Dimensionality reduction
  • L13 - Feature selection
  • L14 - Feature extraction
• Part 6: Bayesian methods
  • L15 - Introduction to Bayesian methods for machine learning
  • L16 - Applying naive Bayes

Part 1: Introduction

L01 - Course overview, introduction to machine learning

L02 - Introduction to Supervised Learning and k-Nearest Neighbors Classifiers

Part 2: Computational foundations

L03 - Using Python

L04 - Introduction to Python’s scientific computing stack

L05 - Data preprocessing and machine learning with scikit-learn

Part 3: Tree-based methods

L06 - Decision trees

L07 - Ensemble methods

Part 4: Model evaluation

L08 - Model evaluation 1 – overfitting

L09 - Model evaluation 2 – confidence intervals

L10 - Model evaluation 3 – cross-validation and model selection

L11 - Model evaluation 4 – algorithm selection

L12 - Model evaluation 5 – evaluation and performance metrics

Part 5: Dimensionality reduction

L13 - Feature selection

L14 - Feature extraction

TBD: I am planning to add more videos some time in the future when time permits. You can subscribe to my YouTube channel to get notified.

Part 6: Bayesian methods

L15 - Introduction to Bayesian methods for machine learning

TBD

L16 - Applying naive Bayes

TBD