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Udemy - Machine Learning (with Claude Code)

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Udemy - Machine Learning (with Claude Code) (Size: 3.3 GB)
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ~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 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 KB
  15. Chapter 2 Fundmental Concepts (Closing).mp4 21 MB
  15. Chapter 2 Fundmental Concepts (Closing)_en-US.srt 1.2 KB
  16. Chapter 3 Algorithms (Opening).mp4 16 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 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 - 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 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 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 MB
  36. Chapter 3-1 Fundamental Concepts_en-US.srt 4.2 KB
  37. Chapter 3-2 Topic Modelling.mp4 51 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
  4 - Module 3 - Reinforcement Learning
  47. Chapter 1 Fundamental Concepts (Opening).mp4 19.4 MB
  47. Chapter 1 Fundamental Concepts (Opening)_en-US.srt 1.2 KB
  48. Chapter 1-1 Agent Environment Interface.mp4 63 MB
  48. Chapter 1-1 Agent Environment Interface_en-US.srt 5.8 KB
  49. Chapter 1-2 Episodic vs Continuous Tasks.mp4 53.1 MB
  49. Chapter 1-2 Episodic vs Continuous Tasks_en-US.srt 5.2 KB
  5 - Pair Engineering (with Claude Code) and Companion Website
  6 - Conclusion
  77. Course Closing.mp4 33.4 MB
  77. Course Closing_en-US.srt 2 KB
  68. Chapter 1 Home.mp4 10.8 MB
  68. Chapter 1 Home_en-US.srt 921.6 B
  68. Companion Website.url 102.4 B
  69. Chapter 2 Getting Started.mp4 14.6 MB
  69. Chapter 2 Getting Started_en-US.srt 2 KB
  69. Companion Website.url 102.4 B
  70. Chapter 3 Claude Code Orientation.mp4 23.2 MB
  70. Chapter 3 Claude Code Orientation_en-US.srt 2.8 KB
  70. Companion Website.url 102.4 B
  71. Chapter 4 Lessons Orientation.mp4 17.7 MB
  71. Chapter 4 Lessons Orientation_en-US.srt 2.3 KB
  71. Companion Website.url 102.4 B
  72. Chapter 5 Module 1 Supervised Learning.mp4 24.7 MB
  72. Chapter 5 Module 1 Supervised Learning_en-US.srt 2.2 KB
  72. Companion Website.url 102.4 B
  73. Chapter 6 Module 2 Unsupervised Learning.mp4 22.1 MB
  73. Chapter 6 Module 2 Unsupervised Learning_en-US.srt 2.7 KB
  73. Companion Website.url 102.4 B
  74. Chapter 7 Module 3 Reinforcement Learning.mp4 19.1 MB
  74. Chapter 7 Module 3 Reinforcement Learning_en-US.srt 2.6 KB
  74. Companion Website.url 102.4 B
  75. Chapter 8 Advisor Agents.mp4 36.5 MB
  75. Chapter 8 Advisor Agents_en-US.srt 2.1 KB
  75. Companion Website.url 102.4 B
  76. Chapter 9 Let's Get Going.mp4 38.9 MB
  76. Chapter 9 Let's Get Going_en-US.srt 2.6 KB
  76. Companion Website.url 102.4 B
  50. Chapter 1-3 Policy and Value Functions.mp4 48.5 MB
  50. Chapter 1-3 Policy and Value Functions_en-US.srt 5 KB
  51. Chapter 1-4 Exploration vs Exploitation.mp4 73.3 MB
  51. Chapter 1-4 Exploration vs Exploitation_en-US.srt 7.3 KB
  52. Chapter 1-5 Applications.mp4 35.6 MB
  52. Chapter 1-5 Applications_en-US.srt 3.4 KB
  53. Chapter 1 Fundamental Concepts (Closing).mp4 22.1 MB
  53. Chapter 1 Fundamental Concepts (Closing)_en-US.srt 1.3 KB
  53. Lab 1 - Multi-Armed Bandit.pdf 611.8 KB
  54. Chapter 2 Tabular RL Methods (Opening).mp4 18.2 MB
  54. Chapter 2 Tabular RL Methods (Opening)_en-US.srt 1.1 KB
  55. Chapter 2-1 Tabular Methods.mp4 86.1 MB
  55. Chapter 2-1 Tabular Methods_en-US.srt 6.7 KB
  56. Chapter 2-2 Monte Carlo and Temporal Difference.mp4 57.5 MB
  56. Chapter 2-2 Monte Carlo and Temporal Difference_en-US.srt 5 KB
  57. Chapter 2-3 SARSA and Q-Learning.mp4 67.6 MB
  57. Chapter 2-3 SARSA and Q-Learning_en-US.srt 5 KB
  58. Chapter 2-4 Frozen Lake Environment.mp4 77.5 MB
  58. Chapter 2-4 Frozen Lake Environment_en-US.srt 6.2 KB
  59. Chapter 2-5 Hyper-parameters and Limitations.mp4 72.6 MB
  59. Chapter 2-5 Hyper-parameters and Limitations_en-US.srt 5 KB
  60. Chapter 2 Tabular RL Methods (Closing).mp4 23.5 MB
  60. Chapter 2 Tabular RL Methods (Closing)_en-US.srt 1.4 KB
  60. Lab 2 - Q-Learning on FrozenLake.pdf 529.1 KB
  61. Chapter 3 Deep Reinforcement Learning (Opening).mp4 19.7 MB
  61. Chapter 3 Deep Reinforcement Learning (Opening)_en-US.srt 1.2 KB
  62. Chapter 3-1 Scaling Problem.mp4 94.5 MB
  62. Chapter 3-1 Scaling Problem_en-US.srt 7.4 KB
  63. Chapter 3-2 DQN Experience Replay and Target Network.mp4 77.1 MB
  63. Chapter 3-2 DQN Experience Replay and Target Network_en-US.srt 6.4 KB
  64. Chapter 3-3 Cart Pole and Policy Gradients.mp4 97 MB
  64. Chapter 3-3 Cart Pole and Policy Gradients_en-US.srt 7.6 KB
  65. Chapter 3-4 Actor Critic and PPO Methods.mp4 72.4 MB
  65. Chapter 3-4 Actor Critic and PPO Methods_en-US.srt 6.8 KB
  66. Chapter 3-5 Algorithm Selection and Applications.mp4 69 MB
  66. Chapter 3-5 Algorithm Selection and Applications_en-US.srt 5.3 KB
  67. Chapter 3 Deep Reinforcement Learning (Closing).mp4 23 MB
  67. Chapter 3 Deep Reinforcement Learning (Closing)_en-US.srt 1.4 KB
  67. Lab 3a - DQN on CartPole.pdf 528.5 KB
  67. Lab 3b - PPO with Stable-Baselines3.pdf 581 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 102.4 B
  40. Lab 3b - Topic Modelling.pdf 494 KB
  41. Chapter 4 Graph Analytics (Opening).mp4 24.3 MB
  41. Chapter 4 Graph Analytics (Opening)_en-US.srt 1.5 KB
  42. Chapter 4-1 Fundamental Concepts.mp4 54.7 MB
  42. Chapter 4-1 Fundamental Concepts_en-US.srt 6.3 KB
  43. Chapter 4-2 Applications.mp4 37 MB
  43. Chapter 4-2 Applications_en-US.srt 3.8 KB
  44. Chapter 4-3 Centrality, Clustering and Density.mp4 41.4 MB
  44. Chapter 4-3 Centrality, Clustering and Density_en-US.srt 4.6 KB
  45. Chapter 4-4 Examples.mp4 61.2 MB
  45. Chapter 4-4 Examples_en-US.srt 6 KB
  46. Chapter 4 Graph Analytics (Closing).mp4 36.9 MB
  46. Chapter 4 Graph Analytics (Closing)_en-US.srt 2.2 KB
  46. Lab 4a - Facebook Dataset.txt 834.3 KB
  46. Lab 4a - Social Network Analysis.pdf 393.7 KB
  46. Lab 4b - Airlines Dataset.csv 638.2 KB
  46. Lab 4b - Flights Analysis.pdf 352.2 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 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

Description


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

⚡ 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.

⚡ Apply unsupervised techniques, K-means clustering, PCA, topic modelling, and graph analytics, to find hidden structure in unlabelled data.

⚡ Implement deep Q-networks with experience replay and target networks, the two engineering fixes that make deep reinforcement learning stable.

⚡ Train reinforcement learning agents from scratch: Q-learning on FrozenLake, a DQN in PyTorch on CartPole, and PPO via Stable-Baselines3.

⚡ Select the right ML paradigm and algorithm for any problem and explain your model choices clearly to both technical and non-technical audiences.

⚡ 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.

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