Machine Learning Crash Course for Beginners using Python [UdemyLibrary.com]

磁力链接/BT种子名称

Machine Learning Crash Course for Beginners using Python [UdemyLibrary.com]

磁力链接/BT种子简介

种子哈希：570b2dc73326f26bb7f968f80431f5bb595020ef
文件大小： 3.3G
已经下载：478次
下载速度：极快
收录时间：2025-03-10
最近下载：2025-09-28

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文件列表

  • 12. Hands-on Machine Learning Project Using Scikit-Learn/4. Face Recognition Project with Python.mp4 213.8 MB
  • 13. OPTIONAL Section- Mathematics Wrap-up/1. Mathematical Wrap-up on Machine Learning.mp4 195.9 MB
  • 5. Data Preparation and Preprocessing/4. One Hot Encoding.mp4 139.7 MB
  • 4. Machine Learning Methods/2. Features Practice with Python.mp4 116.7 MB
  • 8. Overfitting, Underfitting and Generalization/3. Regularization.mp4 114.2 MB
  • 10. Dimensionality Reduction/1. The Curse of Dimensionality.mp4 110.1 MB
  • 8. Overfitting, Underfitting and Generalization/5. Data Snooping and the Test Set.mp4 105.6 MB
  • 7. Building Machine Learning Model from Scratch/6. K-means Clustering from Scratch- Part 2.mp4 105.5 MB
  • 4. Machine Learning Methods/4. Regression Practice with Python.mp4 105.0 MB
  • 12. Hands-on Machine Learning Project Using Scikit-Learn/3. Cross-validation with Python.mp4 96.1 MB
  • 7. Building Machine Learning Model from Scratch/2. Linear Regression from Scratch- Part 2.mp4 95.4 MB
  • 5. Data Preparation and Preprocessing/5. Data Standardization.mp4 94.8 MB
  • 4. Machine Learning Methods/1. Features.mp4 87.1 MB
  • 6. Machine Learning Models and Optimization/2. Machine Learning Model 2.mp4 81.6 MB
  • 12. Hands-on Machine Learning Project Using Scikit-Learn/1. Principal Component Analysis (PCA) with Python.mp4 80.0 MB
  • 8. Overfitting, Underfitting and Generalization/2. Overfitting example on Python.mp4 79.5 MB
  • 3. Process of Learning from Data/1. Supervised Learning.mp4 78.4 MB
  • 12. Hands-on Machine Learning Project Using Scikit-Learn/2. Pipeline in Scikit-Learn for Machine Learning Project.mp4 76.7 MB
  • 2. Why Machine Learning/2. Machine Learning Applications-Part 2.mp4 76.6 MB
  • 6. Machine Learning Models and Optimization/5. Optimization.mp4 74.8 MB
  • 3. Process of Learning from Data/2. UnSupervised Learning and Reinforcement Learning.mp4 73.5 MB
  • 2. Why Machine Learning/1. Machine Learning Applications-Part 1.mp4 71.2 MB
  • 8. Overfitting, Underfitting and Generalization/4. Generalization.mp4 71.1 MB
  • 5. Data Preparation and Preprocessing/3. Handling Text Data.mp4 63.3 MB
  • 8. Overfitting, Underfitting and Generalization/6. Cross-validation.mp4 60.6 MB
  • 6. Machine Learning Models and Optimization/3. Machine Learning Model 3.mp4 58.8 MB
  • 6. Machine Learning Models and Optimization/4. Training Process, Error, Cost and Loss.mp4 58.2 MB
  • 10. Dimensionality Reduction/2. The Principal Component Analysis (PCA).mp4 54.7 MB
  • 11. Deep Learning Overview/1. Introduction to Deep Neural Networks (DNN).mp4 53.8 MB
  • 5. Data Preparation and Preprocessing/2. Handling Video and Audio Data.mp4 49.7 MB
  • 9. Machine Learning Model Performance Metrics/2. The Confusion Matrix.mp4 49.5 MB
  • 1. Introduction to the Course/3. Python Practical of the Course.mp4 48.2 MB
  • 7. Building Machine Learning Model from Scratch/4. Minimun-to-mean Distance Classifier from Scratch- Part 2.mp4 47.6 MB
  • 11. Deep Learning Overview/3. Introduction to Recurrent Neural Networks (CNN).mp4 46.8 MB
  • 6. Machine Learning Models and Optimization/1. Machine Learning Model 1.mp4 46.4 MB
  • 8. Overfitting, Underfitting and Generalization/1. Overfitting Introduction.mp4 42.6 MB
  • 5. Data Preparation and Preprocessing/1. Handling Image Data.mp4 42.3 MB
  • 4. Machine Learning Methods/8. Clustering Practice with Python.mp4 41.1 MB
  • 4. Machine Learning Methods/6. Classification Practice with Python.mp4 39.4 MB
  • 2. Why Machine Learning/3. Why Machine Learning is Trending Now.mp4 36.2 MB
  • 9. Machine Learning Model Performance Metrics/1. The Accuracy.mp4 36.0 MB
  • 7. Building Machine Learning Model from Scratch/3. Minimun-to-mean Distance Classifier from Scratch- Part 1.mp4 35.2 MB
  • 7. Building Machine Learning Model from Scratch/1. Linear Regression from Scratch- Part 1.mp4 34.7 MB
  • 1. Introduction to the Course/2. Focus of the Course.mp4 34.2 MB
  • 7. Building Machine Learning Model from Scratch/5. K-means Clustering from Scratch- Part 1.mp4 32.5 MB
  • 11. Deep Learning Overview/2. Introduction to Convolutional Neural Networks (CNN).mp4 32.0 MB
  • 12. Hands-on Machine Learning Project Using Scikit-Learn/5. THANK YOU Bonus Video.mp4 31.2 MB
  • 1. Introduction to the Course/4. Your Feedback and Review.mp4 30.3 MB
  • 4. Machine Learning Methods/3. Regression.mp4 27.2 MB
  • 1. Introduction to the Course/1. Introduction to the Course.mp4 25.0 MB
  • 4. Machine Learning Methods/7. Clustering.mp4 23.7 MB
  • 4. Machine Learning Methods/5. Classsification.mp4 21.6 MB
  • 13. OPTIONAL Section- Mathematics Wrap-up/1. Mathematical Wrap-up on Machine Learning.srt 49.5 kB
  • 12. Hands-on Machine Learning Project Using Scikit-Learn/4. Face Recognition Project with Python.srt 34.7 kB
  • 4. Machine Learning Methods/4. Regression Practice with Python.srt 24.4 kB
  • 5. Data Preparation and Preprocessing/4. One Hot Encoding.srt 22.5 kB
  • 4. Machine Learning Methods/2. Features Practice with Python.srt 19.3 kB
  • 7. Building Machine Learning Model from Scratch/6. K-means Clustering from Scratch- Part 2.srt 18.1 kB
  • 7. Building Machine Learning Model from Scratch/2. Linear Regression from Scratch- Part 2.srt 18.1 kB
  • 5. Data Preparation and Preprocessing/5. Data Standardization.srt 18.0 kB
  • 8. Overfitting, Underfitting and Generalization/3. Regularization.srt 18.0 kB
  • 10. Dimensionality Reduction/1. The Curse of Dimensionality.srt 16.3 kB
  • 4. Machine Learning Methods/1. Features.srt 16.1 kB
  • 6. Machine Learning Models and Optimization/5. Optimization.srt 16.1 kB
  • 8. Overfitting, Underfitting and Generalization/5. Data Snooping and the Test Set.srt 15.5 kB
  • 6. Machine Learning Models and Optimization/2. Machine Learning Model 2.srt 15.5 kB
  • 8. Overfitting, Underfitting and Generalization/2. Overfitting example on Python.srt 15.3 kB
  • 12. Hands-on Machine Learning Project Using Scikit-Learn/3. Cross-validation with Python.srt 14.9 kB
  • 8. Overfitting, Underfitting and Generalization/6. Cross-validation.srt 13.6 kB
  • 3. Process of Learning from Data/2. UnSupervised Learning and Reinforcement Learning.srt 13.5 kB
  • 1. Introduction to the Course/2. Focus of the Course.srt 13.2 kB
  • 8. Overfitting, Underfitting and Generalization/4. Generalization.srt 12.7 kB
  • 12. Hands-on Machine Learning Project Using Scikit-Learn/2. Pipeline in Scikit-Learn for Machine Learning Project.srt 12.6 kB
  • 12. Hands-on Machine Learning Project Using Scikit-Learn/1. Principal Component Analysis (PCA) with Python.srt 12.3 kB
  • 6. Machine Learning Models and Optimization/1. Machine Learning Model 1.srt 12.2 kB
  • 3. Process of Learning from Data/1. Supervised Learning.srt 12.2 kB
  • 6. Machine Learning Models and Optimization/3. Machine Learning Model 3.srt 12.1 kB
  • 11. Deep Learning Overview/3. Introduction to Recurrent Neural Networks (CNN).srt 12.0 kB
  • 5. Data Preparation and Preprocessing/2. Handling Video and Audio Data.srt 11.8 kB
  • 11. Deep Learning Overview/1. Introduction to Deep Neural Networks (DNN).srt 11.7 kB
  • 10. Dimensionality Reduction/2. The Principal Component Analysis (PCA).srt 11.5 kB
  • 2. Why Machine Learning/2. Machine Learning Applications-Part 2.srt 11.2 kB
  • 6. Machine Learning Models and Optimization/4. Training Process, Error, Cost and Loss.srt 11.2 kB
  • 2. Why Machine Learning/1. Machine Learning Applications-Part 1.srt 11.0 kB
  • 2. Why Machine Learning/3. Why Machine Learning is Trending Now.srt 10.6 kB
  • 5. Data Preparation and Preprocessing/3. Handling Text Data.srt 10.6 kB
  • 5. Data Preparation and Preprocessing/1. Handling Image Data.srt 9.5 kB
  • 9. Machine Learning Model Performance Metrics/2. The Confusion Matrix.srt 9.4 kB
  • 7. Building Machine Learning Model from Scratch/1. Linear Regression from Scratch- Part 1.srt 9.2 kB
  • 1. Introduction to the Course/3. Python Practical of the Course.srt 9.2 kB
  • 7. Building Machine Learning Model from Scratch/4. Minimun-to-mean Distance Classifier from Scratch- Part 2.srt 8.7 kB
  • 7. Building Machine Learning Model from Scratch/3. Minimun-to-mean Distance Classifier from Scratch- Part 1.srt 8.6 kB
  • 4. Machine Learning Methods/8. Clustering Practice with Python.srt 8.1 kB
  • 7. Building Machine Learning Model from Scratch/5. K-means Clustering from Scratch- Part 1.srt 8.0 kB
  • 11. Deep Learning Overview/2. Introduction to Convolutional Neural Networks (CNN).srt 7.6 kB
  • 9. Machine Learning Model Performance Metrics/1. The Accuracy.srt 7.1 kB
  • 8. Overfitting, Underfitting and Generalization/1. Overfitting Introduction.srt 7.1 kB
  • 4. Machine Learning Methods/6. Classification Practice with Python.srt 6.9 kB
  • 4. Machine Learning Methods/5. Classsification.srt 5.9 kB
  • 4. Machine Learning Methods/7. Clustering.srt 5.8 kB
  • 4. Machine Learning Methods/3. Regression.srt 4.9 kB
  • 1. Introduction to the Course/1. Introduction to the Course.srt 4.2 kB
  • 12. Hands-on Machine Learning Project Using Scikit-Learn/5. THANK YOU Bonus Video.srt 2.6 kB
  • 1. Introduction to the Course/4. Your Feedback and Review.srt 2.5 kB
  • 1. Introduction to the Course/5. Link to the Python codes for the projects and the data.html 744 Bytes
  • [UdemyLibrary.com] Join for free courses and tutorials.txt 246 Bytes

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