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ML from Zero: Machine Learning, Math & Python from Scratch [9/2026, ENG]

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ML from Zero: Machine Learning, Math & Python from Scratch [9/2026, ENG] (Size: 1.07 GB)
  1 - Introduction
  1. Introduction, Setup & Reading the Math.html 23.28 KB
  1. What-From-Scratch-Means.mp4 12.99 MB
  2. Reading-ML-Math.mp4 12.29 MB
  3. Setup.mp4 9.05 MB
  4. How-to-use-this-course.mp4 9.34 MB
  10 - PCA
  10. PCA & Dimensionality Reduction.html 25.06 KB
  48. Curse-Of-Dimensionality.mp4 17.33 MB
  49. Covariance-Eigenvectors.mp4 19.14 MB
  50. Implementation.mp4 14.7 MB
  51. Explained-Variance.mp4 16.38 MB
  52. Project-Digits.mp4 17.15 MB
  11 - Neural-Networks
  11. Neural Networks from Scratch.html 24.67 KB
  53. The-Neuron.mp4 14.14 MB
  54. Forward-Propagation.mp4 17 MB
  55. Activations.mp4 16.37 MB
  56. Backpropagation.mp4 20.3 MB
  57. Implementation.mp4 17.14 MB
  58. Training-Loop.mp4 19.52 MB
  59. Project-Mnist.mp4 15.22 MB
  12 - Capstone
  12. Capstone & Real-World Workflow.html 25.34 KB
  60. The-Workflow.mp4 13.97 MB
  61. ProjectA-Eda.mp4 15.07 MB
  62. ProjectA-Modeling.mp4 17.52 MB
  63. ProjectB-Classifier.mp4 19.58 MB
  64. ProjectC-Segmentation.mp4 20.71 MB
  65. Benchmark.mp4 35.43 MB
  66. Where-Next.mp4 21.3 MB
  2 - Math-Toolkit
  10. Numpy-Vectorization.mp4 14.77 MB
  2. The Math Toolkit for ML.html 24.48 KB
  5. Vectors-Dot-Product.mp4 13.84 MB
  6. Matrices.mp4 15.26 MB
  7. Derivatives.mp4 14.57 MB
  8. Gradients.mp4 14.17 MB
  9. Probability.mp4 14.65 MB
  3 - Linear-Regression
  11. The-Model.mp4 12.27 MB
  12. Cost-Function.mp4 15.2 MB
  13. Normal-Equation.mp4 15.02 MB
  14. Implementation.mp4 15.01 MB
  15. Project-House-Prices.mp4 17.64 MB
  16. Metrics-&-Validation.mp4 15.19 MB
  3. Linear Regression from Scratch.html 24.94 KB
  4 - Gradient-Descent
  17. Why-Iterative.mp4 15.33 MB
  18. Deriving-The-Gradient.mp4 15.22 MB
  19. Learning-Rate.mp4 14.18 MB
  20. Batch-Stochastic-Minibatch.mp4 15.77 MB
  21. Project-Optimize-Regressor.mp4 16.75 MB
  4. Gradient Descent.html 24.6 KB
  5 - Logistic-Regression
  22. Sigmoid.mp4 15.62 MB
  23. Cross-Entropy.mp4 19 MB
  24. Log-Loss-Gradient.mp4 15.09 MB
  25. Implementation.mp4 18.2 MB
  26. Project-Breast-Cancer.mp4 17.23 MB
  27. Classification-Metrics.mp4 19.19 MB
  5. Logistic Regression & Classification.html 24.64 KB
  6 - KNN
  28. Distance-Metrics.mp4 17.39 MB
  29. Implementation.mp4 14.48 MB
  30. Vectorizing-And-Choosing-K.mp4 16.34 MB
  31. Project-Wine.mp4 14.53 MB
  6. K-Nearest Neighbors.html 24.89 KB
  7 - Decision-Trees
  32. How-Trees-Split.mp4 15.25 MB
  33. Entropy-Gini-Gain.mp4 21.49 MB
  34. Building-The-Tree.mp4 17.21 MB
  35. Overfitting-And-Pruning.mp4 15.2 MB
  36. Random-Forest.mp4 16.99 MB
  37. Project-Titanic.mp4 16.91 MB
  7. Decision Trees & Random Forests.html 24.09 KB
  8 - Naive-Bayes
  38. Bayes-Theorem.mp4 15.18 MB
  39. The-Naive-Assumption.mp4 14.93 MB
  40. Multinomial-Nb.mp4 15.04 MB
  41. Project-Spam.mp4 17.59 MB
  42. Gaussian-Nb.mp4 17.67 MB
  8. Naive Bayes.html 25.07 KB
  9 - Kmeans
  43. Unsupervised-And-Inertia.mp4 15.28 MB
  44. Lloyds-Algorithm.mp4 14.89 MB
  45. Implementation.mp4 18.63 MB
  46. Choosing-K.mp4 17.18 MB
  47. Project-Segmentation.mp4 29.19 MB
  9. K-Means Clustering.html 24.8 KB

Description


Обучающие видео » Компьютерные видеоуроки и обучающие интерактивные DVD » Machine/Deep Learning, Neural Networks

ML from Zero: Machine Learning, Math & Python from Scratch

Год выпуска: 9/2026
Производитель: Udemy
Сайт производителя: https://www.udemy.com/course/ml-from-zero-machine-learning-math-python-from-scratch/
Автор: Vaibhav Kumar Singh
Продолжительность: 6h 34m
Тип раздаваемого материала: Видеоурок
Язык: Английский
Описание: This course teaches machine learning from zero by having you write each core algorithm yourself in Python before comparing it with a professional library, so you understand why a model works instead of calling a function and hoping for the best. It explains each idea in plain language first, then as a formula, then as a few lines of code, so no maths degree is required—the formulas behind machine learning become tools you can use rather than symbols to memorize. You will learn Python and NumPy for machine learning including arrays, vectorization, broadcasting, and generating data; linear algebra essentials with vectors, the dot product, matrices, and matrix multiplication; calculus for ML covering derivatives, partial derivatives, the chain rule, and gradient descent; probability and statistics including mean, variance, distributions, and Bayes rule; linear regression from scratch with gradient descent and the normal equation; logistic regression and classification including the sigmoid and log loss; model evaluation with train/test split, confusion matrix, precision, recall, regularization, and cross-validation; and beyond the basics with k-nearest neighbors, k-means clustering, and how decision trees choose a split.

The course works through short, focused video lectures with captions, code walkthroughs you can follow line by line, companion Python scripts and Jupyter notebooks for every module, and from-scratch code checked against scikit-learn so you can see your version gives the same answers. It is for beginners who want to understand machine learning properly, programmers who want the maths explained through code, and students who find ML textbooks hard to follow—if you know basic Python and want to know what happens inside a model, this course is for you. By the end, you will be able to read a machine learning formula, implement it in Python, and judge whether a model is working. The course uses AI tools to support content creation.
[spoiler="Содержание"]
1 - Introduction
1. What-From-Scratch-Means (04:41)
2. Reading-ML-Math (05:53)
3. Setup (03:11)
4. How-to-use-this-course (03:34)

2 - Math-Toolkit
5. Vectors-Dot-Product (05:25)
6. Matrices (05:35)
7. Derivatives (05:48)
8. Gradients (05:09)
9. Probability (05:54)
10. Numpy-Vectorization (05:21)

3 - Linear-Regression
11. The-Model (04:41)
12. Cost-Function (05:57)
13. Normal-Equation (04:55)
14. Implementation (04:55)
15. Project-House-Prices (06:34)
16. Metrics-&-Validation (06:00)

4 - Gradient-Descent
17. Why-Iterative (05:49)
18. Deriving-The-Gradient (05:52)
19. Learning-Rate (06:19)
20. Batch-Stochastic-Minibatch (06:09)
21. Project-Optimize-Regressor (06:06)

5 - Logistic-Regression
22. Sigmoid (05:57)
23. Cross-Entropy (06:41)
24. Log-Loss-Gradient (05:32)
25. Implementation (06:15)
26. Project-Breast-Cancer (06:09)
27. Classification-Metrics (06:46)

6 - KNN
28. Distance-Metrics (06:36)
29. Implementation (05:25)
30. Vectorizing-And-Choosing-K (06:03)
31. Project-Wine (05:52)

7 - Decision-Trees
32. How-Trees-Split (05:56)
33. Entropy-Gini-Gain (07:25)
34. Building-The-Tree (06:49)
35. Overfitting-And-Pruning (05:36)
36. Random-Forest (06:32)
37. Project-Titanic (06:37)

8 - Naive-Bayes
38. Bayes-Theorem (05:36)
39. The-Naive-Assumption (05:31)
40. Multinomial-Nb (05:56)
41. Project-Spam (07:01)
42. Gaussian-Nb (06:28)

9 - Kmeans
43. Unsupervised-And-Inertia (05:49)
44. Lloyds-Algorithm (05:43)
45. Implementation (06:09)
46. Choosing-K (06:33)
47. Project-Segmentation (05:45)

10 - PCA
48. Curse-Of-Dimensionality (06:27)
49. Covariance-Eigenvectors (06:22)
50. Implementation (05:32)
51. Explained-Variance (06:30)
52. Project-Digits (05:58)

11 - Neural-Networks
53. The-Neuron (05:47)
54. Forward-Propagation (05:56)
55. Activations (06:30)
56. Backpropagation (06:45)
57. Implementation (05:59)
58. Training-Loop (06:35)
59. Project-Mnist (06:16)

12 - Capstone
60. The-Workflow (06:13)
61. ProjectA-Eda (06:01)
62. ProjectA-Modeling (06:32)
63. ProjectB-Classifier (06:37)
64. ProjectC-Segmentation (06:23)
65. Benchmark (06:23)
66. Where-Next (07:11)
[/spoiler]
Файлы примеров: не предусмотрены
Формат видео: MP4
Видео: AVC, 1920x1080, 16:9, 30fps, 231kbps
Аудио: AAC, 44.1Hz, 128kbps, stereo
[spoiler="Скриншоты"]







[/spoiler]

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