Udemy - Machine Learning (with Claude Code)

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size3.3 GB
  • Uploaded Byfreecoursewb
  • Downloads14
  • Last checkedOct. 02nd '26
  • Date uploadedOct. 02nd '26
  • Seeders 1
  • Leechers20

Infohash : E6442DF71C7AA069AABCCD4EFEC53BD5B58FBA5B

Machine Learning (with Claude Code)

https://WebToolTip.com

Published 9/2026
Created by John Poh
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English + subtitle | Duration: 77 Lectures ( 4h 2m ) | Size: 3.3 GB

From Statistical Foundations to Applied Intelligence

What you'll learn
âš¡ Build and evaluate supervised ML models, logistic regression, SVM, random forests, KNN, on real datasets using Python and scikit-learn.
⚡ Derive the statistical foundations of ML, bias, variance, MSE, maximum likelihood — that explain why supervised models work and when they fail.
⚡ Apply the bias–variance tradeoff, cross-validation, and metrics beyond accuracy (AUC-ROC, F1) to judge and defend whether a model is trustworthy.
âš¡ Apply unsupervised techniques, K-means clustering, PCA, topic modelling, and graph analytics, to find hidden structure in unlabelled data.
âš¡ Implement deep Q-networks with experience replay and target networks, the two engineering fixes that make deep reinforcement learning stable.
âš¡ Train reinforcement learning agents from scratch: Q-learning on FrozenLake, a DQN in PyTorch on CartPole, and PPO via Stable-Baselines3.
âš¡ Select the right ML paradigm and algorithm for any problem and explain your model choices clearly to both technical and non-technical audiences.
âš¡ Use Claude Code as an AI pair programmer to build, debug, and interpret ML models alongside specialist advisor agents.

Requirements
❗ Basic Python is required. You should be comfortable with variables, functions, loops, and importing libraries. No advanced Python needed.
❗ Familiarity with pandas and numpy, enough to load a CSV and run basic array operations. If you're rusty, a one-hour refresher before Module 1 is sufficient.
❗ A working terminal. You need to be able to open a terminal, navigate folders with cd, and run a Python script. No sysadmin experience required.
❗ Node.js installed, needed to install Claude Code (npm install -g @anthropic-ai/claude-code). Free and takes under five minutes to set up.
❗ No prior machine learning experience required, every concept is introduced from first principles with analogies before any mathematics.
❗ No advanced mathematics required. A basic grasp of mean, variance, and what a function is will get you through. All statistical concepts are built up from scratch as they arise.
❗ macOS, Linux, or Windows (WSL2). The labs run in a terminal environment. Windows users should have WSL2 set up; instructions are provided in the course setup guide.

Files:

[ WebToolTip.com ] Udemy - Machine Learning (with Claude Code)
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction
    • 1. Course Opening.mp4 (31.1 MB)
    • 1. Course Opening_en-US.srt (1.9 KB)
    • 1. Course Orientation and Overview.pdf (322.4 KB)
    2 - Module 1 - Supervised Learning
    • 10. Chapter 2-1 Model as an Estimator.mp4 (57.4 MB)
    • 10. Chapter 2-1 Model as an Estimator_en-US.srt (6.5 KB)
    • 11. Chapter 2-2 Overfitting vs Underfitting.mp4 (53.1 MB)
    • 11. Chapter 2-2 Overfitting vs Underfitting_en-US.srt (5.7 KB)
    • 12. Chapter 2-3 Curse of Dimensionality.mp4 (41.5 MB)
    • 12. Chapter 2-3 Curse of Dimensionality_en-US.srt (5.0 KB)
    • 13. Chapter 2-4 Ensemble Methods.mp4 (48.8 MB)
    • 13. Chapter 2-4 Ensemble Methods_en-US.srt (5.5 KB)
    • 14. Chapter 2-5 Evaluation and Metrics.mp4 (47.4 MB)
    • 14. Chapter 2-5 Evaluation and Metrics_en-US.srt (5.0 KB)
    • 15. Chapter 2 Fundmental Concepts (Closing).mp4 (21.0 MB)
    • 15. Chapter 2 Fundmental Concepts (Closing)_en-US.srt (1.2 KB)
    • 16. Chapter 3 Algorithms (Opening).mp4 (16.0 MB)
    • 16. Chapter 3 Algorithms (Opening)_en-US.srt (1.1 KB)
    • 17. Chapter 3-1 Logistic Regression.mp4 (69.6 MB)
    • 17. Chapter 3-1 Logistic Regression_en-US.srt (8.0 KB)
    • 17. Lab 1 - Logistic Regression.pdf (465.4 KB)
    • 17. Lab 1 - Titanic Dataset.csv (58.9 KB)
    • 18. Chapter 3-2 Support Vector Machines.mp4 (39.6 MB)
    • 18. Chapter 3-2 Support Vector Machines_en-US.srt (4.3 KB)
    • 18. Lab 2 - Support Vector Machine.pdf (440.1 KB)
    • 19. Chapter 3-3 Decision Trees and Random Forest.mp4 (63.7 MB)
    • 19. Chapter 3-3 Decision Trees and Random Forest_en-US.srt (6.4 KB)
    • 19. Lab 3 - Decision Tree and Random Forest.pdf (492.2 KB)
    • 19. Lab 3 - Loans Dataset.csv (733.6 KB)
    • 2. Chapter 1 Estimation Theory (Opening).mp4 (16.5 MB)
    • 2. Chapter 1 Estimation Theory (Opening)_en-US.srt (1.1 KB)
    • 20. Chapter 3-4 Naive Bayes, K Nearest Neighbours and Linear Regression.mp4 (78.7 MB)
    • 20. Chapter 3-4 Naive Bayes, K Nearest Neighbours and Linear Regression_en-US.srt (8.6 KB)
    • 20. Lab 4 - Naive Bayes.pdf (335.6 KB)
    • 20. Lab 4 - Wines Dataset.csv (11.2 KB)
    • 20. Lab 5 - Anonymised Dataset.csv (189.8 KB)
    • 20. Lab 5 - K Nearest Neighbours.pdf (376.1 KB)
    • 21. Chapter 3-5 Feature Selection.mp4 (63.1 MB)
    • 21. Chapter 3-5 Feature Selection_en-US.srt (6.4 KB)
    • 22. Chapter 3 Algorithms (Closing).mp4 (23.6 MB)
    • 22. Chapter 3 Algorithms (Closing)_en-US.srt (1.4 KB)
    • 3. Chapter 1-1 What is Estimation.mp4 (59.6 MB)
    • 3. Chapter 1-1 What is Estimation_en-US.srt (7.5 KB)
    • 4. Chapter 1-2 Properties of Estimators.mp4 (58.6 MB)
    • 4. Chapter 1-2 Properties of Estimators_en-US.srt (7.3 KB)
    • 5. Chapter 1-3 Mean Squared Error and the Bias Variance Tradeoff.mp4 (55.2 MB)
    • 5. Chapter 1-3 Mean Squared Error and the Bias Variance Tradeoff_en-US.srt (6.4 KB)
    • 6. Chapter 1-4 Three Classical Estimation Methods.mp4 (49.6 MB)
    • 6. Chapter 1-4 Three Classical Estimation Methods_en-US.srt (6.5 KB)
    • 7. Chapter 1-5 From Estimation Theory to Machine Learning.mp4 (28.6 MB)
    • 7. Chapter 1-5 From Estimation Theory to Machine Learning_en-US.srt (3.6 KB)
    • 8. Chapter 1 Estimation Theory (Closing).mp4 (21.0 MB)
    • 8. Chapter 1 Estimation Theory (Closing)_en-US.srt (1.3 KB)
    • 9. Chapter 2 Fundamental Concepts (Opening).mp4 (16.8 MB)
    • 9. Chapter 2 Fundamental Concepts (Opening)_en-US.srt (1.1 KB)
    3 - Module 2 - Unsupervised Learning
    • 23. Chapter 1 Cluster Analysis (Opening).mp4 (19.9 MB)
    • 23. Chapter 1 Cluster Analysis (Opening)_en-US.srt (1.3 KB)
    • 24. Chapter 1-1 Fundamental Concepts.mp4 (11.3 MB)
    • 24. Chapter 1-1 Fundamental Concepts_en-US.srt (2.9 KB)
    • 25. Chapter 1-2 K Means Algorithm.mp4 (54.6 MB)
    • 25. Chapter 1-2 K Means Algorithm_en-US.srt (6.1 KB)
    • 26. Chapter 1-3 K Means Examples and Applications.mp4 (63.6 MB)
    • 26. Chapter 1-3 K Means Examples and Applications_en-US.srt (7.1 KB)
    • 27. Chapter 1-4 Choosing K and Limitations.mp4 (73.4 MB)
    • 27. Chapter 1-4 Choosing K and Limitations_en-US.srt (8.3 KB)
    • 28. Chapter 1 Cluster Analysis (Closing).mp4 (26.2 MB)
    • 28. Chapter 1 Cluster Analysis (Closing)_en-US.srt (1.3 KB)
    • 28. Lab 1 - College Dataset.csv (76.2 KB)
    • 28. Lab 1 - KMeans Cluster Analysis (Universities).pdf (467.4 KB)
    • 29. Chapter 2 Principal Component Analysis (Opening).mp4 (24.0 MB)
    • 29. Chapter 2 Principal Component Analysis (Opening)_en-US.srt (1.4 KB)
    • 30. Chapter 2-1 Fundamantal Concepts.mp4 (26.5 MB)
    • 30. Chapter 2-1 Fundamantal Concepts_en-US.srt (3.3 KB)
    • 31. Chapter 2-2 PCA in Five Steps.mp4 (52.2 MB)
    • 31. Chapter 2-2 PCA in Five Steps_en-US.srt (6.0 KB)
    • 32. Chapter 2-3 Examples.mp4 (59.7 MB)
    • 32. Chapter 2-3 Examples_en-US.srt (6.8 KB)
    • 33. Chapter 2-4 Applications and Limitations.mp4 (40.6 MB)
    • 33. Chapter 2-4 Applications and Limitations_en-US.srt (4.8 KB)
    • 34. Chapter 2 Principal Component Analysis (Closing).mp4 (29.9 MB)
    • 34. Chapter 2 Principal Component Analysis (Closing)_en-US.srt (1.8 KB)
    • 34. Lab 2 - Principal Component Analysis.pdf (385.7 KB)
    • 34. Lab 2 - Wines Dataset.csv (11.2 KB)
    • 35. Chapter 3 Natural Language Processing (Opening).mp4 (23.9 MB)
    • 35. Chapter 3 Natural Language Processing (Opening)_en-US.srt (1.4 KB)
    • 36. Chapter 3-1 Fundamental Concepts.mp4 (34.0 MB)
    • 36. Chapter 3-1 Fundamental Concepts_en-US.srt (4.2 KB)
    • 37. Chapter 3-2 Topic Modelling.mp4 (51.0 MB)
    • 37. Chapter 3-2 Topic Modelling_en-US.srt (6.3 KB)
    • 38. Chapter 3-3 Latent Dirichlet Allocation.mp4 (32.3 MB)
    • 38. Chapter 3-3 Latent Dirichlet Allocation_en-US.srt (4.4 KB)
    • 39. Chapter 3-4 Examples and Applications.mp4 (54.4 MB)
    • 39. Chapter 3-4 Examples and Applications_en-US.srt (5.9 KB)
    • 40. Chapter 3 Natural Language Processing (Closing).mp4 (29.9 MB)
    • 40. Chapter 3 Natural Language Processing (Closing)_en-US.srt (1.9 KB)
    • 40. Lab 3a - NLP Fundamentals.pdf (289.8 KB)
    • 40. Lab 3b - Papers Dataset.url (0.1 KB)
    • 40. Lab 3

Code:

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