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[ FreeCourseWeb ] Udemy - Machine Learning with SciKit-Learn with Python

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[ FreeCourseWeb ] Udemy - Machine Learning with SciKit-Learn with Python (Size: 3.6 GB)
  1. Cross Validation.mp4 112 MB
  1. Cross Validation.srt 17.6 KB
  1. Intro to Pandas.mp4 37.5 MB
  1. Intro to Pandas.srt 10.5 KB
  1. Introduction to Machine Learning.mp4 27.8 MB
  1. Introduction to Machine Learning.srt 7.5 KB
  1. Introduction to Matlplotlib.mp4 27.5 MB
  1. Introduction to Matlplotlib.srt 5.9 KB
  1. Introduction to Scikit Learn.mp4 48.8 MB
  1. Introduction to Scikit Learn.srt 12 KB
  1. Movie Review Screen Stream.mp4 67.8 MB
  1. Movie Review Screen Stream.srt 11.1 KB
  1. NumPy Array Creation.mp4 55.2 MB
  1. NumPy Array Creation.srt 12.5 KB
  1. NumPy Array Indexing.mp4 61.8 MB
  1. NumPy Array Indexing.srt 12 KB
  1. NumPy Introduction.mp4 35.8 MB
  1. NumPy Introduction.srt 9.6 KB
  1. Scikit Example Digits.mp4 45.2 MB
  1. Scikit Example Digits.srt 10.3 KB
  10. Copies and Views.mp4 56.1 MB
  10. Copies and Views.srt 7.6 KB
  10. Extracting Features.mp4 46.9 MB
  10. Extracting Features.srt 9.4 KB
  11. Occurrences to Frequencies.mp4 77.1 MB
  11. Occurrences to Frequencies.srt 12.8 KB
  12. Classifier Training.mp4 61 MB
  12. Classifier Training.srt 8.1 KB
  13. Performance Analysis on the Test Set.mp4 119.4 MB
  13. Performance Analysis on the Test Set.srt 15.3 KB
  14. Parameter Tuning.mp4 85.1 MB
  14. Parameter Tuning.srt 13.4 KB
  15. Language Identifcation.mp4 92.9 MB
  15. Language Identifcation.srt 17.8 KB
  2. Advantages and Disadvantages of Machine Learning.mp4 26.9 MB
  2. Advantages and Disadvantages of Machine Learning.srt 10.9 KB
  2. Cross Validation Techniques.mp4 52.2 MB
  2. Cross Validation Techniques.srt 8.9 KB
  2. Digits Dataset Using Matplotlib.mp4 62.8 MB
  2. Digits Dataset Using Matplotlib.srt 9.6 KB
  2. Features and Installation.mp4 56.8 MB
  2. Features and Installation.srt 8.5 KB
  2. Intro to Pandas Continue.mp4 42.6 MB
  2. Intro to Pandas Continue.srt 11.2 KB
  2. Movie Review Screen Stream Continue.mp4 37.6 MB
  2. Movie Review Screen Stream Continue.srt 5.3 KB
  2. NumPy Array Attributes.mp4 56 MB
  2. NumPy Array Attributes.srt 9.8 KB
  2. NumPy Array Indexing Continue.mp4 35.4 MB
  2. NumPy Array Indexing Continue.srt 6.2 KB
  2. Supervised.mp4 52.5 MB
  2. Supervised.srt 13.3 KB
  2. Understanding Various Functions of Pyplot.mp4 85.4 MB
  2. Understanding Various Functions of Pyplot.srt 13.8 KB
  3. Data Structure in Pandas.mp4 83.9 MB
  3. Data Structure in Pandas.srt 12.1 KB
  3. K-Means Clustering Example.mp4 104 MB
  3. Multiple Figures and Subplots.mp4 110.3 MB
  3. Multiple Figures and Subplots.srt 13.9 KB
  3. NumPy Array Boolean.mp4 66.9 MB
  3. NumPy Array Boolean.srt 12.5 KB
  3. NumPy Array Operations.mp4 78.5 MB
  3. NumPy Array Operations.srt 14.3 KB
  3. Understading Metrics of Predicted Digits Dataset.mp4 42 MB
  3. Understading Metrics of Predicted Digits Dataset.srt 8 KB
  3. Unsupervised Learning.mp4 53.3 MB
  3. Unsupervised Learning.srt 10.7 KB
  4. Agglomeration.mp4 76.6 MB
  4. Agglomeration.srt 12.8 KB
  4. Data Structure in Pandas Continue.mp4 102.2 MB
  4. Data Structure in Pandas Continue.srt 16.4 KB
  4. Load Data Set.mp4 43.4 MB
  4. Load Data Set.srt 7.9 KB
  4. NumPy Array Operations Continue.mp4 93 MB
  4. NumPy Array Operations Continue.srt 14.3 KB
  4. Persisting Models.mp4 105.2 MB
  4. Persisting Models.srt 17.7 KB
  5. K-NN Algorithm with Example.mp4 101.4 MB
  5. K-NN Algorithm with Example.srt 20.3 KB
  5. NumPy Array Unary Operations.mp4 35.5 MB
  5. NumPy Array Unary Operations.srt 6.8 KB
  5. PCA Pipeline.mp4 117.2 MB
  5. PCA Pipeline.srt 19.2 KB
  5. Pandas Column Select.mp4 79.6 MB
  5. Pandas Column Select.srt 13.3 KB
  6. Face Recognition.mp4 53.6 MB
  6. Face Recognition.srt 8.6 KB
  6. Numpy Array Splicing.mp4 98.8 MB
  6. Numpy Array Splicing.srt 14.2 KB
  6. Remove Operations.mp4 117.4 MB
  6. Remove Operations.srt 11.3 KB
  7. Face Recognition Output.mp4 52.1 MB
  7. NumPy Array Shpe.mp4 67.3 MB
  7. NumPy Array Shpe.srt 13.8 KB
  7. Pandas Arithmetic Operations.mp4 103.2 MB
  7. Pandas Arithmetic Operations.srt 14.1 KB
  8. Pandas Arithmetic Operations Continue.mp4 49.7 MB
  8. Pandas Arithmetic Operations Continue.srt 8.6 KB
  8. Right Estimator.mp4 59.9 MB
  8. Right Estimator.srt 9 KB
  8. Stacking Together Different Arrays.mp4 85.5 MB
  8. Stacking Together Different Arrays.srt 11.4 KB
  9. Splitting one Array into Several Smaller ones.mp4 43.7 MB
  9. Splitting one Array into Several Smaller ones.srt 8.1 KB
  9. Text Data Example.mp4 89.8 MB
  9. Text Data Example.srt 16.2 KB
  Bonus Resources.txt 307.2 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 108 total files

Description


Udemy- Machine Learning with SciKit-Learn with Python



MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 54 lectures (8 hour, 23 mins) | Size: 3.59 GB
Get a practical understanding of the Scikit-Learn library and learn the ML implementation
What you'll learn

This Scikit-learn Training has been designed in a manner so that it can contain all the topics that the trainees have to expertise so that they can work effectively with this library. At the starting of the course, you will get to learn about Machine Learning with SciKit-Learn which is one of the important components of this course where you will be learning every single thing about SciKit-Learn.
You will be getting deep exposure to python in this training. Once you are done with this course, you will be possessing an ample skillset to work efficiently with the SciKit-Learn library.

Requirements

Several topics or concepts are there for which you should have a basic understanding of to make the learning of this library easy for you. The very first thing is the basics of python. As this library is entirely based on python, the trainees need to have a basic understanding of the concepts of python. If you would have worked with python, you will find the concepts covered here pretty simple.
The next important concept is the basics of Machine learning. With the help of this library, we will be implementing the concepts of ML. So it is very necessary to understand how it could be used. In this Scikit-learn Training, we have included all the topics that we are considering as the prerequisite here so that the trainees can brush up their understanding before beginning this training.

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