Udemy - The Complete Machine Learning Course with Python

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[FreeAllCourse.Com] Udemy- The Complete Machine Learning Course with Python 1. Introduction

1. What Does the Course Cover.mp4 (54.4 MB)
1. What Does the Course Cover.vtt (3.0 KB)
2. How to Succeed in This Course.html (2.2 KB)
3. Project Files and Resources.html (1.7 KB)

10. Unsupervised Learning Clustering

1. Clustering.mp4 (125.7 MB)
1. Clustering.vtt (18.7 KB)
2. k_Means Clustering.mp4 (57.7 MB)
2. k_Means Clustering.vtt (10.0 KB)

11. Deep Learning

1. Estimating Simple Function with Neural Networks.mp4 (143.9 MB)
1. Estimating Simple Function with Neural Networks.vtt (24.4 KB)
2. Neural Network Architecture.mp4 (22.4 MB)
2. Neural Network Architecture.vtt (7.2 KB)
3. Motivational Example - Project MNIST.mp4 (145.0 MB)
3. Motivational Example - Project MNIST.vtt (23.5 KB)
4. Binary Classification Problem.mp4 (72.1 MB)
4. Binary Classification Problem.vtt (11.5 KB)
5. Natural Language Processing - Binary Classification.mp4 (76.0 MB)
5. Natural Language Processing - Binary Classification.vtt (11.7 KB)

12. Appendix A1 Foundations of Deep Learning

1. Introduction to Neural Networks.mp4 (13.8 MB)
1. Introduction to Neural Networks.vtt (2.5 KB)
10. Gradient Based Optimization.mp4 (55.0 MB)
10. Gradient Based Optimization.vtt (12.6 KB)
11. Getting Started with Neural Network and Deep Learning Libraries.mp4 (18.7 MB)
11. Getting Started with Neural Network and Deep Learning Libraries.vtt (5.1 KB)
12. Categories of Machine Learning.mp4 (37.5 MB)
12. Categories of Machine Learning.vtt (11.2 KB)
13. Over and Under Fitting.mp4 (70.1 MB)
13. Over and Under Fitting.vtt (16.7 KB)
14. Machine Learning Workflow.mp4 (27.4 MB)
14. Machine Learning Workflow.vtt (5.3 KB)
2. Differences between Classical Programming and Machine Learning.mp4 (20.9 MB)
2. Differences between Classical Programming and Machine Learning.vtt (4.9 KB)
3. Learning Representations.mp4 (77.2 MB)
3. Learning Representations.vtt (11.5 KB)
4. What is Deep Learning.mp4 (155.6 MB)
4. What is Deep Learning.vtt (23.1 KB)
5. Learning Neural Networks.mp4 (40.6 MB)
5. Learning Neural Networks.vtt (11.4 KB)
6. Why Now.mp4 (9.1 MB)
6. Why Now.vtt (3.0 KB)
7. Building Block Introduction.mp4 (14.2 MB)
7. Building Block Introduction.vtt (5.1 KB)
8. Tensors.mp4 (16.9 MB)
8. Tensors.vtt (4.3 KB)
9. Tensor Operations.mp4 (88.8 MB)
9. Tensor Operations.vtt (18.9 KB)

13. Computer Vision and Convolutional Neural Network (CNN)

1. Outline.mp4 (63.7 MB)
1. Outline.vtt (4.1 KB)
10. Training Your CNN 1.mp4 (124.9 MB)
10. Training Your CNN 1.vtt (15.2 KB)
11. Training Your CNN 2.mp4 (128.5 MB)
11. Training Your CNN 2.vtt (22.4 KB)
12. Loading Previously Trained Model.mp4 (11.2 MB)
12. Loading Previously Trained Model.vtt (1.6 KB)
13. Model Performance Comparison.mp4 (79.8 MB)
13. Model Performance Comparison.vtt (10.7 KB)
14. Data Augmentation.mp4 (28.5 MB)
14. Data Augmentation.vtt (3.3 KB)
15. Transfer Learning.mp4 (97.0 MB)
15. Transfer Learning.vtt (12.1 KB)
16. Feature Extraction.mp4 (111.1 MB)
16. Feature Extraction.vtt (12.9 KB)
17. State of the Art Tools.mp4 (35.4 MB)
17. State of the Art Tools.vtt (6.0 KB)
2. Neural Network Revision.mp4 (43.8 MB)
2. Neural Network Revision.vtt (9.2 KB)
3. Motivational Example.mp4 (66.2 MB)
3. Motivational Example.vtt (8.7 KB)
4. Visualizing CNN.mp4 (141.9 MB)
4. Visualizing CNN.vtt (15.4 KB)
5. Understanding CNN.mp4 (30.0 MB)
5. Understanding CNN.vtt (6.7 KB)
6. Layer - Input.mp4 (29.1 MB)
6. Layer - Input.vtt (6.2 KB)
7. Layer - Filter.mp4 (84.4 MB)
7. Layer - Filter.vtt (18.5 KB)
8. Activation Function.mp4 (32.3 MB)
8. Activation Function.vtt (6.9 KB)
9. Pooling, Flatten, Dense.mp4 (88.1 MB)
9. Pooling, Flatten, Dense.vtt (12.5 KB)

2. Getting Started with Anaconda

1. Installing Applications and Creating Environment.mp4 (38.4 MB)
1. Installing Applications and Creating Environment.vtt (6.0 KB)
2. Hello World.mp4 (51.2 MB)
2. Hello World.vtt (12.5 KB)
3. Iris Project 1 Working with Error Messages.mp4 (89.8 MB)
3. Iris Project 1 Working with Error Messages.vtt (14.5 KB)
4. Iris Project 2 Reading CSV Data into Memory.mp4 (64.6 MB)
4. Iris Project 2 Reading CSV Data into Memory.vtt (10.0 KB)
5. Iris Project 3 Loading data from Seaborn.mp4 (55.9 MB)
5. Iris Project 3 Loading data from Seaborn.vtt (9.9 KB)
6. Iris Project 4 Visualization.mp4 (93.5 MB)
6. Iris Project 4 Visualization.vtt (11.5 KB)

3. Regression

1. Scikit-Learn.mp4 (48.5 MB)
1. Scikit-Learn.vtt (10.0 KB)
10. Multiple Regression 2.mp4 (91.2 MB)
10. Multiple Regression 2.vtt (13.8 KB)
11. Regularized Regression.mp4 (44.3 MB)
11. Regularized Regression.vtt (7.8 KB)
12. Polynomial Regression.mp4 (110.8 MB)
12. Polynomial Regression.vtt (19.7 KB)
13. Dealing with Non-linear Relationships.mp4 (62.7 MB)
13. Dealing with Non-linear Relationships.vtt (10.3 KB)
14. Feature Importance.mp4 (36.3 MB)
14. Feature Importance.vtt (5.4 KB)
15. Data Preprocessing.mp4 (135.5 MB)
15. Data Preprocessing.vtt (25.5 KB)

Description

The Complete Machine Learning Course with Python

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

Created by Codestars by Rob Percival, Anthony NG, Rob Percival
Last updated 11/2019
English

What you'll learn

Machine Learning Engineers earn on average $166,000 - become an ideal candidate with this course!
Solve any problem in your business, job or personal life with powerful Machine Learning models
Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning etc

For More Courses Visit: https://FreeAllCourse.com